Aalen, O. (1978), Non-parametric inference for a family of counting processes, Annals of
Statistics 6, 710–726.
Abramowitz, M. & Stegun, I. A. (1970), Eds., Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, Applied Mathematics Series 55, National Bureau of Standards, Washington, DC. ninth printing.
Andersen, E. (1970), Sufficiency and exponential families for discrete sample spaces, Journal of the American Statistical Association 65, 1248–1255.
Andersen, S. (1957), On the collective theory of risk in case of contagion between claims, Bulletin of the Institute of Mathematics and its Applications 12, 2775–279.
Anderson, R. (2007), The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation, Oxford University Press, New York.
Ang, A. & Piazzesi, M. (2003), A no-arbitrage vector autoregression of term structure dynamics with macroecconomic and latent variables, Journal of Monetary Economics
50(4), 745–787.
Antonio, K., Frees, E. & Valdez, E. (2010), A multilevel analysis of intercompany claim counts, ASTIN Bulletin: The Journal of the International Actuarial Association
40(1), 151–177.
Antonio, K. & Valdez, E. (2012), Statistical aspects of a priori and a posteriori risk classification in insurance, Advances in Statistical Analysis 96(2), 187–224.
Antonio, K. & Zhang, W. (2014), Predictive Modelling in Actuarial Science, Cambridge University Press, chapter Mixed models for predictive modelling in actuarial science.
Archer, K. J. (2010), rpartOrdinal: An R package for deriving a classification tree for predicting an ordinal response, Journal of Statistical Software 34(7), 1–17.
Arnold, B. C. (1983), Pareto Distributions, International Co-operative Publishing House, Fairland, MD.
Artzner, P., Delbaen, F., Eber, J.-M. & Heath, D. (1999), Coherent measures of risk, Math- ematical Finance 9(3), 203–228.
Asmussen, S. & Albrecher, H. (2010), Ruin Probabilities, 2nd edn., World Scientific, Hack- ensack, NJ.
Asmussen, S. & Rolski, T. (1991), Computational methods in risk theory: A matrix- algorithmic approach, Insurance: Mathematics and Economics 10, 259–274.
Baier, T. & Neuwirth, E. (2003), High-level interface between R and Excel, in K. Hornik, F. Leisch & A. Zeileis, Eds., Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), Vienna, Austria.
Bailey, R. (1963), Insurance rates with minimum bias, Proceedings of the Society of Actuaries 50, 4–11.
Baillie, R. T. & Bollerslev, T. (1989), Common stochastic trends in a system of exchange rates, Journal of Finance 44(1), 167–181.
Banerjee, S., Carlin, B. P. & Gelfand, A. E. (2004), Hierachical Modelling and Analysis for Spatial Data, Chapman and Hall, Boca Raton, Florida.
Bank for International Settlements (2005), Zero-coupon yield curves: Technical documentation, BIS papers 25, Monetary and Economic Department.
Barbi, E. (1999), Eterogeneit`a della Popolazione e Sopravvivenza Umana: Prospettive Metodologiche ed Applicazioni alle Generazioni Italiane 1870–1895, PhD thesis, Florence.
Bardis, M., Majidi, A. & Murphy, D. (2012), A family of chain-ladder factor models for selected link ratios, Variance 2, 143–160.
Barnett, G. & Zehnwirth, B. (2000), Best estimates for reserves, Proceedings of the CAS LXXXVII(167).
Bates, D. (2003), Converting packages to S4, R News 3(1), 6–8.
Bates, D. (2005), Fitting linear mixed models in R, R News 5(1), 27–30.
Bates, D. & Maechler, M. (2012), Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.0-6.
Becker, R. A. (1994), A brief history of S, Technical report, AT&T Bell Laboratories, Murray Hil, NJ.
Becker, R. A. & Chambers, J. M. (1984), S. An Interactive Environment for Data Analysis and Graphics, Wadsworth and Brooks/Cole, Monterey, CA. [the ‘brown book’].
Beirlant, J., Goegebeur, Y., Teugels, J. & Segers, J. (2004), Statistics of Extremes, Wiley Series in Probability and Statistics, John Wiley & Sons Ltd., Chichester. Theory and applications, With contributions from Daniel De Waal and Chris Ferro.
Beirlant, J. & Teugels, J. (1992), Modelling large claims in non-life insurance, Insurance: Mathematics and Economics 11, 17–29.
Ben-Israel, A. & Greville, T. N. E. (2003), Generalized Inverses: Theory and Applications, Springer, New York.
Berger, J. (1985), Bayesian Inference in Statistical Analysis, Springer-Verlag, Berlin. Berger, J. O., Bernardo, J. M. & Sun, D. (2009), The formal definition of reference priors, Annals of Statistics 37, 905–938.
Bernegger, S. (1997), The Swiss Re exposure curves and the MBBEFD distribution class, ASTIN Bulletin 27(1), 99–111.
Besag, J. (1974), Spatial interaction and the statistical analysis of lattice data systems (with discussion), Journal of the Royal Statistical Society, Series B 36, 192–225.
Bickel, P. & Doksum, K. (2001), Mathematical Statistics: Basic Ideas and Selected Topics, Vol. 1, Prentice Hall, Englewood Cliffs, NJ.
Biller, B. & Nelson, B. (2003), Modeling and generating multivariate time-series input processes using a vector autoregressive technique, ACM Transactions on Modeling and Computer Simulation 13(3), 211–237.
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. (2013), Applied Spatial Data Analysis with R, Second edition, Springer, New York.
Black, F. & Litterman, R. (1992), Global portfolio optimization, Financial Analysts Journal 48(5), 28–43.
Bladt, M. (2005), A review on phase-type distributions and their use in risk theory, ASTIN Bulletin 35(1), 145–161.
Blanpain, N. & Chardon, O. (2010), Projections de populations 2007-2060 pour la France m´etropolitaine: M´ethode et principaux r´esultats, S´erie des Documents de Travail de la direction des statistiques D´emographiques et Sociales F1008, Institut National de la Statistique et des E´ tudes E´ conomiques.
Blue, J., Fox, P., Fullerton, W., Gay, D., Grosse, E., Hall, A., Kaufman, L., Petersen, W. & Schryer, N. (1978), PORT Mathematical Subroutine Library, Lucent Technologies Murray Hill, NJ.
Bollerslev, T. (1986), Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31(3), 307–327.
Bollerslev, T. (1987), A conditionally heteroskedastic time series model for speculative prices and rates of return, The Review of Economics and Statistics pp. 542–547.
Bollerslev, T., Chou, R. Y. & Kroner, K. F. (1992), ARCH modeling in finance: A review of the theory and empirical evidence, Journal of Econometrics 52(1), 5–59.
Bollerslev, T. & Ghysels, E. (1996), Periodic autoregressive conditional heteroscedasticity, Journal of Business & Economic Statistics 14(2), 139–151.
Booth, H. (2006), Demographic forecasting: 1980 to 2005 in review, International Journal of Forecasting 22(3), 547–581.
Booth, H., Hyndman, R. J., Tickle, L. & de Jong, P. (2006), Lee-Carter mortality forecasting: A multi-country comparison of variants and extensions, Demographic Research
15(9), 289–310.
Booth, H., Maindonald, J. & Smith, L. (2002), Applying Lee-Carter under conditions of variable mortality decline, Population Studies 56(3), 325–336.
Booth, H. & Tickle, L. (2008), Mortality modelling and forecasting: A review of methods,Annals of Actuarial Science 3(1-2), 3–43.
Booth, H., Tickle, L. & Smith, L. (2005), Evaluation of the variants of the Lee-Carter method of forecasting mortality: A multi-country comparison, New Zealand Population Review 31(1), 13–37. Special Issue on Stochastic Population Projections, edited by A. Dharmalingam & I. Pool.
Bornhuetter, R. L. & Ferguson, R. E. (1972), The actuary and IBNR, Proceedings of the Casualty Actuarial Society pp. 181–195.
Boston, C. S. F. (1997), Creditrisk+: A credit risk management framework, Technical report, Credit Suisse First Boston, New York.
Bowers, N. L., Jones, D. A., Gerber, H. U., Nesbitt, C. J. & Hickman, J. C. (1997), Actuarial
Mathematics, 2nd edition, Society of Actuaries, Schaumburg, IL.
Bowman, A. & Azzalini, A. (1997), Applied Smoothing Techniques for Data Analysis, Oxford
University Press, London.
Breiman, L. (1996), Bagging predictors, Machine Learning 24(2), 123–140.
Breiman, L., Friedman, J., Olshen, R. & Stone, C. (1984), Classification and Regression Trees, Wadsworth, New York.
Brillinger, D. R. (1986), The Natural variability of vital rates and associated statistics, Biometrics 42, 693–734.
Brouhns, N., Denuit, M. & Vermunt, J. K. (2002), A Poisson log-bilinear regression approach to the construction of projected lifetables, Insurance: Mathematics and Economics
31(3), 373–393.
Broyden, C. G. (1970), The convergence of a class of double-rank minimization algorithms, Journal of the Institute of Mathematics and Its Applications 6, 76–90.
Bu¨hlmann, H. (1969), Experience rating and credibility, ASTIN Bulletin 5, 157–165. Bu¨hlmann, H. & Gisler, A. (2005), A Course in Credibility Theory and Its Applications, Springer, Berlin.
Butt, Z. & Haberman, S. (2009), ilc: A collection of R functions for fitting a class of Lee- Carter mortality models using iterative fitting algorithms, Actuarial Research Paper 190, Cass Business School, London.
Cairns, A. (2009), A quantitative comparison of stochastic mortality models using data from England and Wales and the United States, North American Actuarial Journal 13(1), 1–
35.
Cairns, A. J., Blake, D. & Dowd, K. (2006a), A two-factor model for stochastic mortality with parameter uncertainty: Theory and calibration, Journal of Risk and Insurance 73(4), 687–718.
Cairns, A. J. G., Blake, D. & Dowd, K. (2006b), Pricing death: Frameworks for the valuation and securitization of mortality risk, ASTIN Bulletin 36, 79–120.
Camarda, C. G. (2012), MortalitySmooth: An R package for smoothing Poisson counts with P-splines, Journal of Statistical Software 50(1), 1–24.
Cameron, A.C. & Trivedi, P. (1998), Regression Analysis of Counts Data, Cambridge University Press, New York.
Cardot, H., Ferraty, F. & Sarda, P. (2003), Spline estimators for the functional linear model, Statistica Sinica 13(3), 571–591.
Carl, P. & Peterson, B. G. (2013), PerformanceAnalytics: Econometric tools for performance and risk analysis. R package version 1.1.0.
http://CRAN.R-project.org/package=PerformanceAnalytics
Casella, G. & Berger, R. (2002), Statistical Inference, Duxbury Thomson Learning, N. Scituate, MA.
Chalabi, Y. (2012), New Directions in Statistical Distributions, Parametric Modeling and Portfolio Selection, PhD thesis, Eidgen¨ossische Technische Hochschule ETH Zu¨rich. Chambers, J. M. & Hastie, T. (1991), Statistical Models in S, Chapman & Hall, London.
Chambers, J. M. & Lang, D. T. (2001), Object-oriented programming in R, R News 1(3), 17–19.
Chant, D. (1974), On asymptotic tests of composite hypotheses in nonstandard conditions, Biometrika 61, 291–298.
Cherubini, U., Luciano, E. & Vecchiato, W. (2004), Copula Methods in Finance, Wiley, New York.
Chiang, C. L. (1984), The Life Table and Its Applications, Robert E. Krieger Publishing, Malabar, FL.
Ching, W. & Ng, M. (2006), Markov Chains: Models, Algorithms and Applications: Models, Algorithms and Applications, International series in Operations Research & Management Science, Springer Verlag, Berlin.
Chow, G. (1960), Tests of equality between sets of coefficients in two linear regressions, Econometrica pp. 591–605.
Christofides, S. (1997), Regression models based on log-incremental payments, Claims Reserving Manual 2, D5.1–D5.53.
Clark, D. R. (2003), LDF Curve-Fitting and Stochastic Reserving: A Maximum Likelihood Approach, Casualty Actuarial Society. CAS Fall Forum.
Clarke, A. (1973), Profiles of the Future: An Inquiry into the Limits of the Possible, Millennium Edition.
Cleveland, W. S. (1993), Visualizing Data, Hobart Press, Summit, NJ.
Coale, A., Demeny, P. & Vaughan, B. (1983), Regional Model Life Tables and Stable Populations, Studies in Population, Academic Press, New York.
Cohen, Y. & Cohen, J. (2008), Statistics and Data with R: An Applied Approach through Examples, Wiley, New York.
Coles, S. G. (2001), An Introduction to Statistical Modelling of Extreme Values, Springer, London.
Consiglio, A. & Guirreri, S. (2011), Simulating term structure of interest rates with arbitrary marginals, International Journal of Risk Assessment and Management 15(4).
Cormen, T. H., Leiserson, C. E. & Rivest, R. L. (1989), Introduction to Algorithms, MIT Press, Cambridge, MA.
Coughlan, G. D., Epstein, D., Ong, A., Sinha, A., Hevia-Portocarrero, J., Gingrich, E., Khalaf-Allah, M. & Joseph, P. (2007), LifeMetrics: A Toolkit for Measuring and Managing Longevity and Mortality Risks. Version 1.1: 13 March 2007. JP Morgan Pension Advisory Group.
Courant, R. & Hilbert, D. (2009), R through Excel, Springer Verlag, Berlin.
Cox, J., Ingersoll, J. & Ross, S. (1985), A theory of the term structure of interest rates, Econometrica 53, 385–407.
Cox, J. R. (1972), Regression models and life-tables (with discussion), Journal of Royal Stat. Ser. B, 187–220.
Cram´er, H. (1946), Mathematical Methods of Statistics, Princeton University Press, Princeton, NJ.
Crawley, M. (2012), The R Book, Wiley Interscience, New York.
Csardi, G. & Nepusz, T. (2006), The igraph software package for complex network research,
InterJournal p. 1695.
Cullen, A. & Frey, H. (1999), Probabilistic Techniques in Exposure Assessment, Springer, New York.
Culp, M., Johnson, K. & Michailidis, G. (2006), ada: An R package for stochastic boosting,
Journal of Statistical Software 17(2), 1–27.
Currie, I. D. (2006), Smoothing and forecasting mortality rates with P-splines, Heriot Watt
University, London.
Currie, I. D., Durb´an, M. & Eilers, P. H. C. (2004), Smoothing and forecasting mortality rates, Statistical Modelling 4(4), 279–298.
D’Agostino, R. & Stephens, M. (1986), Goodness-of-Fit Techniques, first edn., Dekker, New
York.
Dalgaard, P. (2009), Introductory Statistics with R, Springer Verlag, Berlin.
Daniel, J.W. (2004), Multi-state transition models with actuarial applications, http://www.casact.org/library/studynotes/daniel.pdf.
De Jong, P. & Tickle, L. (2006), Extending LeeCarter Mortality Forecasting, Mathematical
Population Studies 13(1), 1–18.
De Jong, P. & Zeller, G. (2008), Generalized Linear Models for Insurance Data, Cambridge
University Press, New York.
Deb´on, A., Montes, F. & Sala, R. (2006), A comparison of nonparametric methods in the graduation of mortality: Application to data from the Valencia region (Spain), International Statistical Review 74(2), 215–233.
Delgado, M. & Kniesner, T. (1997), Count data models with variance of unknown form: An application to a hedonic model of worker absenteeism, The Review of Economics and Statistics 79(1), 41–49.
Delignette-Muller, M. & Dutang, C. (2013), fitdistrplus: An R Package for Fitting Distributions. Working paper.
Delignette-Muller, M. L., Pouillot, R., Denis, J.-B. & Dutang, C. (2013), fitdistrplus: Help to Fit of a Parametric Distribution to Non-censored or Censored Data. R package version
1.0-1.
Delwarde, A., Denuit, M., Oli´e, L. & Kachakhidze, D., (2004), Mod`eles lin´eaires et additifs g´eneralis´es, maximum de vraisemblance local et m´ethodes relationelles en assurance sur la vie, Bulletin Fran¸cais d’Actuariat 6(12), 77–102.
Delwarde, A. & Denuit, M. (2005), Construction de tables de mortalite periodiques et prospectives. Assurance Audit Actuariat. Economica.
Denuit, M. & Goderniaux, A. C. (2005), Closing and projecting life tables using log-linear models, Bulletin of the Swiss Association of Actuaries (1), 29–48.
Denuit, M., Marechal, X., Pitrebois, S. & Walhin, J.-F. (2007), Actuarial Modelling of Claim
Counts: Risk Classification, Credibility and Bonus-Malus Systems, Wiley, New York.
Deshmukh, S. (2012), Multiple Decrement Models in Insurance: An Introduction Using R, SpringerLink : Bu¨cher, Springer Verlag.
Dey, D. K., Chen, M. H. & Chang, H. (1997), Bayesian approach for nonlinear random effects models, Biometrics 53, 1239–1252.
Dickson, D. (2010), Insurance Risk and Ruin, Cambridge University Press, London. Dickson, D., Hardy, M. & Waters, H. (2009), Actuarial Mathematics for Life Contingent Risks, International Series on Actuarial Science, Cambridge University Press, London.
Diebold, X. & Li, C. (2006), Forecasting the term structure of government bond yields, Journal of Econometrics 130(2), 337–364.
Diez, D. & Christou, N. (2012), stockPortfolio: Build stock models and analyze stock portfolios. R package version 1.2.
Diggle, P., Heagerty, P., Liang, K. & Zeger, S. (2002), Analysis of Longitudinal Data, 2nd edn, Oxford University Press, London.
Duffie, D. & Kan, R. (1996), A yield-factor model of interest rates, Mathematical Finance 6, 379–406.
Dutang, C., Goulet, V. & Pigeon, M. (2008), actuar: An R package for actuarial science, Journal of Statistical Software 25(7), 1–37.
Dwyer, P. S. (1951), Linear Computations, Wiley, New York.
Eddelbuettel, D. & Fran¸cois, R. (2011), Rcpp: Seamless R and C++ integration, Journal of Statistical Software 40(8), 1–18.
Embrechts, P., Klu¨ppelberg, C. & Mikosch, T. (1997), Modelling Extremal Events, Springer Verlag, Berlin.
Embrechts, P., Lindskog, F. & McNeil, A. (2001), Modelling Dependence with Copulas and applications to Risk Management, Technical report, ETH Zurich.
England P.D. & Verrall R.J. (2002), Stochastic claims reserving in general insurance, British Actuarial Journal 8, 443–544.
Engle, R. F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society pp. 987–1007.
Enos, J., Kane, D., with contributions from Gerlanc, D. & Campbell, K. (2012a), portfolio: Analysing equity portfolios. R package version 0.4-5.
Enos, J., Kane, D. with contributions from Campbell, K. (2012b), portfolioSim: Framework for simulating equity portfolio strategies. R package version 0.2-6.
Enos, J., Kane, D., with contributions from Campbell, K. Gerlanc, D., Schwartz, A., Suo, D., Colin, A., & Zhao, L. (2012c), backtest: Exploring portfolio-based conjectures about financial instruments. R package version 0.3-1.
Estrella, A. & Mishkin, F. S. (1996), The Term Structure of Interest Rates and its Role in Monetary Policy for the European Central Bank, Research Paper 9526, Federal Reserve Bank of New York.
Estrella, A. & Trubin, M. R. (2006), The yield curve as a leading indicator: Some practical issues, Current Issues in Economics and Finance 12(5).
Fang, K.-T., Kotz, S. & Ng, K.-W. (1990), Symmetric Multivariate and Related Distributions, Chapman & Hall, London.
Faraway, J. (2006), Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, CRC Press, Boca Raton, FL.
Feinerer, I. (2008), An introduction to text mining in R, R News 8(2), 19–22.
Felipe, A., Guill´en, M. & P´erez-Mar´ın, A. (2002), Recent mortality trends in the Spanish population, British Actuarial Journal 8(4), 757–786.
Felisky, K., Akoh-Arrey, A. & Cabrera, E. (2010), Solvency II and Technical Provisions Dealing with the Risk Margin, Institute of Actuaries. GIRO conference and exhibition.
Finan, M. (n.d.), A reading of the theory of life contingency models: A preparation for exam mlc, http://faculty.atu.edu/mfinan/actuarieshall/MLCbook2.pdf. Accessed: 01/11/2012.
Firth, D. (2003), Overdispersed Poisson - negative observation. Discussion on R-help.
https://stat.ethz.ch/pipermail/r-help/2003-January/028743.html
Fisher, R. (1940), The precision of discriminant functions, Annals of Eugenics 10, 422–429. Fishman, G. & Moore, L. (1982), A statistical evaluation of multiplicative congruential
random number generators with modulus 2311, Journal of the American Statistical Association 77, 129–136.
Fletcher, R. (1970), A new approach to variable metric algorithms, Computer Journal
13, 317–322.
Forfar, D., McCutcheon, J. & Wilkie, A. (1988), On graduation by mathematical formula,
Journal of the Institute of Actuaries 115(1), 1–459.
Fox, J. (2009), Aspects of the Social Organization and Trajectory of the R Project, The R Journal 1, 5–13.
Fox, J. & Weisberg, S. (2011), An R Companion to Applied Regression, 2nd edn., Sage, Thousand Oaks CA.
Frees, E. (2004), Longitudinal and Panel Data: Analysis and Applications in the Social
Sciences, Cambridge University Press, London.
Frees, E. (2009), Regression Modeling with Actuarial and Financial Applications, Cambridge
University Press, New York.
Frees, E. W. & Valdez, E. (1998), Understanding relationships using copulas, North American Actuarial Journal 2(1), 1–25.
Frees, E. W. & Wang, P. (2006), Copula credibility for aggregate loss models, Insurance: Mathematics and Economics 38, 360–373.
Frees, E. & Wang, P. (2005), Credibility using copulas, North American Actuarial Journal
9(2), 31–48.
Frost, P. A. & Savarino, J. E. (1986), An empirical Bayes approach to efficient portfolio selection, The Journal of Financial and Quantitative Analysis 21, 293–305.
Fu, W. J. (1998), Penalized regressions: The bridge versus the lasso, Journal of Computational and Graphical Statistics 7(3), 397416.
Furnival, G. M. & Wilson, Robert W., J. (1974), Regression by leaps and bounds, Technometrics 16, 499–511.
Galimberti, G., Soffritti, G. & Maso, M. D. (2012), Classification trees for ordinal responses in R: The rpartScore package, Journal of Statistical Software 47(10), 1–25.
Gandrud, C. (2013), Reproducible Research with R and RStudio, Chapman & Hall / CRC Press, Boca Raton, FL.
Geisser, S. & Eddy, W. F. (1979), A predictive approach to model selection (Corr: V75 p765), Journal of the American Statistical Association 74, 153–160.
Gelman, A. & Hill, J. (2007), Data Analysis Using Regression and Multilevel/Hierarchical
Models, Cambridge University Press, New York.
Gendron, M. & Crepeau, H. (1989), On the computation of the aggregate claim distribution when individual claims are inverse Gaussian, Insurance: Mathematics and Economics
8, 251–258.
Genolini, C. (2008), A (Not So) Short Introduction to S4.
URL: christophe.genolini.free.fr/Tutorial/notSoShort.php
Genton, M., ed. (2004), Skew-Elliptical Distributions and Their Applications, Chapman & Hall/CRC, Boca Raton, FL.
Gesmann, M., Murphy, D. & Zhang, Y. (2013), ChainLadder: Mack-, Bootstrap and Munich- Chain-Ladder Methods for Insurance Claims Reserving. R package version 0.1.7.
Gesmann, M., Rayees, R. & Clapham, E. (2013), Claims inflation – A known unknown, The
Actuary, May 2013 pp. 30–31.
Ghalanos, A. (2013), parma: Portfolio Allocation and Risk Management Applications. R
package version 1.03.
Ghalanos, A. & Theussl, S. (2012), Rsolnp: General Non-linear Optimization Using Augmented Lagrange Multiplier Method. R package version 1.14.
Giles, T. L. (1993), Life insurance application of recursive formulas, Journal of Actuarial
Practice 1(2), 141–151.
Gochez, F. (2011), BLCOP: Black-Litterman and Copula-Opinion Pooling Frameworks. R
package version 0.2.6.
Goldberg, D. (1991), What every computer scientist should know about floating-point arithmetic, ACM Comput. Surveys 23(1), 5–48.
Goldfarb, D. (1970), A family of variable metric updates derived by variational means,
Mathematics of Computation 24, 23–26.
Goldfarb, D. & Idnani, A. (1982), Dual and primal-dual methods for solving strictly convex quadratic programs, in Numerical Analysis, Springer Verlag, Berlin, pp. 226–239.
Goldfarb, D. & Idnani, A. (1983), A numerically stable dual method for solving strictly convex quadratic programs, Mathematical Programming 27(1), 1–33.
Goovaerts, M., De Vylder, F. & Haezendonck, J. (1984), Insurance Premiums, North Holland Publishing, Amsterdam.
Graham, R. L., Knuth, D. E. & Patashnik, O. (1989), Concrete Mathematics, Addison- Wesley, Reading, MA.
Gravelsons, B., Ball, M., Beard, D., Brooks, R., Couchman, N., Kefford, C., Michaels, D., Nolan, P., Overton, G., Robertson-Dunn, S., Ruffini, E., Sandhouse, G., Schilling, J., Sykes, D., Taylor, P., Whiting, A., Wilde, M. & Wilson, J. (2009), B12: UK as- bestos working party update 2009’, http://www.actuaries.org.uk/research-and-resources/ documents/b12-uk-asbestos-working-party-update-2009-5mb. Presented at the General Insurance Convention.
Greenwood, A. J. & Durand, D. (1960), Aids for fitting the gamma distribution by maximum likelihood, Technometrics 2, 55–65.
Grothendieck, G. & Petzoldt, T. (2004), R Help Desk: Date and time classes in R, R News
4(1), 29–32.
Guirreri, S. (2010), Simulating Term Structure of Interest Rates with Arbitrary Marginals, PhD thesis, University of Palermo.
Guirreri, S. (2012), YieldCurve: Modelling and Estimation of the Yield Curve. R package version 4.0.
http://www.guirreri.host22.com
Guszcza, J. (2008), Hierarchical Growth Curve Models for Loss Reserving, in CAS Forum, pp. 146–173.
Haberman, S. & Pitacco, E. (1999), Actuarial Models for Disability Insurance, Chapman & Hall/CRC, Boca Raton, FL.
Haberman, S. & Renshaw, A. E. (2011), A comparative study of parametric mortality projection models, Insurance: Mathematics and Economics 48(1), 35–55.
Hachemeister, C. A. & Stanard, J. N. (1975), IBNR Claims Count Estimation with Static
Lag Functions, in 12th ASTIN Colloquium, Portimao, Portugal.
Halton, J. H. (1960), On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals, Numerische Mathematik 2, 84–90.
Hand, D. J. (2005), Good practice in retail credit score-card assessment, Journal of the
Operational Research Society 56, 1109–1117.
Harrell, F. (2006), Problems caused by categorizing continuous variables, available online at http://biostat.mc.vanderbilt.edu /twiki/bin/view/Main/.
Hastie, T. J. & Tibshirani, R. J. (1990), Generalized Additive Models, Chapman & Hall, London.
Hastie, T., Tibshirani, R. & Friedman, J. (2009), Elements of Statistical Learning: Data
Mining, Inference and Prediction, Springer-Verlag, Berlin.
Hausman, J. (1978), Specification tests in econometrics, Econometrica pp. 1251–1271.
He, X. & Ng, P. T. (1999), COBS: Qualitatively constrained smoothing via linear programming’, Computational Statistics 14(3), 315–337.
Heiberger, R. M. & Neuwirth, E. (1953), Methodes of Mathematical Physics, volume 1, Wiley, New York.
Hemmerle, W. J. (1967), Statistical Computations on a Digital Computer, Blaisdell, Waltham, MA.
Henley, W. & Hand, D. (1996), A k-nearest-neighbour classifier for assessing consumer credit risk, The Statistician 45(1), 77–95.
Herzog, T. N. (1996), Introduction to Credibility Theory, second edn., ACTEX, Winsted, CT.
Hilbe, J. (2009), Logistic Regression Models, Chapman & Hall/CRC Press, Boca Raton FL. Hilbe, J. (2011), Negative Binomial Regression, Wiley, New York.
Hill, B. M. (1975), A simple general approach to inference about the tail of a distribution,
Annals of Statistics 3(5), 1163–1174.
Hoerl, A. E. & Kennard, R. W. (1970), Ridge regression: Biased estimation for nonorthogonal problems, Technometrics 42(1), 80–86.
Hoffmann, T. J. (2011), Passing in command line arguments and parallel cluster/multicore batching in R with batch, Journal of Statistical Software, Code Snippets 39(1), 1–11.
Højsgaard, S., Edwards, D. & Lauritzen, S. (2012), Graphical Models with R, Springer
Verlag, Berlin.
Holmes, S. (2006), Review of Fionn Murtagh’s book: Correspondence analysis and data coding with Java and R, R News 6(4), 41–43.
Hornik, K., Meyer, D. & Buchta, C. (2013), slam: Sparse Lightweight Arrays and Matrices. R package version 0.1-28.
Hornik, K. & Theussl, S. (2012), Rglpk: R/GNU Linear Programming Kit Interface. R
package version 0.3-10.
Hosmer, D. & Lemeshow, S. (2000), Applied Logistic Regression, Wiley, New York. Hothorn, T., Bretz, F. & Genz, A. (2001), On multivariate t and Gauß probabilities in R,
R News 1(2), 27–29.
Hsiao, C. (2003), Analysis of Panel Data, 2nd edn., Cambridge University Press, New York. Hsieh, D. A. (1989), Testing for nonlinear dependence in daily foreign exchange rates,
Journal of Business 62, 339–368.
Hubert, M., Rousseeuw, P. J. & Verboven, S. (2002), A fast method of robust principal components with applications to chemometrics, Chemometrics and Intelligent Laboratory Systems 60, 101–111.
Human Mortality Database (2013), University of California, Berkeley (USA), and Max
Planck Institute for Demographic Research (Germany). Downloaded on 22 February
2013.
Hyndman, R. & Fan, Y. (1996), Sample quantiles in statistical packages, American Statistician 50, 361–365.
Hyndman, R. J. (2012), Demography: Forecasting mortality, fertility, migration and population data. With contributions from Heather Booth, Leonie Tickle and John Maindonald.
Hyndman, R. J. & Booth, H. (2008), Stochastic population forecasts using functional data models for mortality, fertility and migration, International Journal of Forecasting
24(3), 323–342.
Hyndman, R. J., Booth, H. & Yasmeen, F. (2013), Coherent mortality forecasting: The product-ratio method with functional time series models, Demography 50(1), 261–283.
Hyndman, R. J. & Khandakar, Y. (2008), Automatic time series forecasting: The forecast package for R, Journal of Statistical Software 27(3), 1-22.
Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008), Forecasting with
Exponential Smoothing: The State Space Approach, Springer-Verlag, Berlin.
Hyndman, R. J. & Shang, H. L. (2009), Forecasting functional time series (with discussion),
Journal of the Korean Statistical Society 38(3), 199–221.
Hyndman, R. J. & Ullah, S. (2007), Robust forecasting of mortality and fertility rates: A
functional data approach, Computational Statistics and Data Analysis 51(10), 4942–4956.
Ihaka, R. & Gentleman, R. (1996), R: A language for data analysis and graphics, Journal of Computational and Graphical Statistics 5(3), 299–314.
Jagger, T. & Elsner, J. (2008), Modelling tropical cyclone intensity with quantile regression,
International Journal of Climatology 29, 1351–1361.
Jakob, K., Fischer, D. M. & Kolb, S. (2013), crp.CSFP: CreditRisk+ portfolio model. R
package version 1.2.1.
Jewell, W. (2004), Bayesian statistics, in J. L. Teugels & B. Sundt, Eds., ‘Encyclopedia of
Actuarial Science’, Vol. 1, Wiley.
Joe, H. (1997), Multivariate models and dependence concepts, in Monographs on Statistics and Applied Probability, Vol. 73, Chapman & Hall, Boca Raton, FL.
Johnson, N. (1949), System of frequency curves generated by methods of translation,
Biometrika 36, 149–176.
Johnson, N., Kemp, A. & Kotz, S. (2005), Univariate Discrete Distributions, 3rd ed., Wiley- Interscience, New York.
Jones, O., Maillardet, R. & Robinson, A. (2009), Introduction to Scientific Programming and Simulation Using R, first ed., Chapman and Hall/CRC Press, Boca Raton, FL.
Jørgensen, B. (1987), Exponential dispersion models, Journal of the Royal Statistical Society, Series B 49, 127–162.
Jørgensen, B. (1997), The Theory of Dispersion Models, Chapman & Hall, London.
Kaas, R., Goovaerts, M., Dhaene, J. & Denuit, M. (2008), Modern Actuarial Risk Theory
– Using R, second ed., Springer-Verlag, Heidelberg.
Kabacoff, R. (2011), R in Action, Manning Publications, Greenwich, CT.
Kahle, D. & Wickham, H. (2013), ggmap: A Package for Spatial Visualization with Google
Maps and OpenStreetMap. R package version 2.3.
Kendrick, D., Mercado, P. & Amman, H. (2006), Computational economics: Help for the underestimated undergraduate, Computational Economics 77, 261–271.
Kernighan, B. W. (1988), C/C++Programming Language, Prentice Hall, Englewood Cliff
NJ.
Kilibarda, M. (2013), plotGoogleMaps: Plot SP or SPT(STDIF,STFDF) Data as HTML Map Mashup over Google Maps. R package version 2.0.
Kleiber, C. & Zeileis, A. (2008), Applied Econometrics with R, Springer-Verlag, New York. Kleinman, K. & Horton, N. J. (2010), SAS and R : Data Management, Statistical Analysis,
and Graphics, Chapman & Hall/CRC, Press Boca Raton, FL.
Klugman, S. (1992), Bayesian Statistics in Actuarial Science: With Emphasis on Credibility, Kluwer, Boston.
Klugman, S. A., Panjer, H. H. & Willmot, G. E. (2009), Loss Models: From Data to Decisions, Wiley Series in Probability and Statistics, New York.
Klugman, S. & Parsa, R. (1999), Fitting bivariate loss distributions with copulas, Insurance: Mathematics and Economics 24, 139–148.
Knorr-Held, L. & Best, N. (2001), A shared component model for detecting joint and selective clustering of two diseases, Journal of the Royal Statistical Society, Series B 164, 73–
85.
Knuth, D. E. (1973), Fundamental Algorithms, Vol. 1 of The Art of Computer Programming, second ed., Addison-Wesley, Reading, MA, section 1.2, pp. 10–119. A full INBOOK entry.
Koissi, M.-C., Shapiro, A. F. & H¨ogn¨as, G. (2006), Evaluating and extending the Lee-Carter model for mortality forecasting: Bootstrap confidence interval, Insurance: Mathematics and Economics 38(1), 1–20.
Krause, A. (2009), The Basics of S-PLUS, Springer Verlag, Berlin.
Kremer, E. (1982), IBNR claims and the two-way model of ANOVA, Scandinavian Actuarial
Journal pp. 47–55.
Kuhn, M. (2008), Building predictive models in R using the caret package, Journal of
Statistical Software 28(5), 1–26.
Lang, D. T. (2001), In search of C/C++ & FORTRAN routines, R News 1(3), 20–23.
Lawrence, M. & Verzani, J. (2012), Programming Graphical User Interfaces in R, Chapman
& Hall / CRC, Press, Boca Raton, FL.
Laws, C. W. & Schmid, F. A. (2012), lossDev: Robust Loss Development Using MCMC. R
package version 3.0.0-4.
Lazar, D. & Denuit, M. M. (2009), A multivariate time series approach to projected life tables, Applied Stochastic Models in Business and Industry 25, 806–823.
Lee, R. D. & Carter, L. R. (1992), Modeling and forecasting U.S. mortality, Journal of the
American Statistical Association 87(419), 659–671.
Lee, R. D. & Miller, T. (2001), Evaluating the performance of the Lee-Carter method for forecasting mortality, Demography 38(4), 537–49.
Leisch, R. (2009), Creating R Packages: A Tutorial.
URL: cran.c project.org/doc/contrib/Leisch-CreatingPackages.pdf.
Lemaire, J. (1984), Bonus-Malus Systems in Automobile Insurance, North-Holland Publishing, Amsterdam.
Lemieux, C. (2009), Monte Carlo and Quasi-Monte Carlo Sampling, Springer-Verlag, New
York.
Liaw, A. & Wiener, M. (2002), Classification and regression by randomForest, R News
2(3), 18–22.
Liddell, F. D. K. (1984), Simple exact analysis of the standardized mortality ratio, Journal of Epidemiology and Community Health 38, 85–88.
Ligges, U. & Fox, J. (2008), R Help Desk: How can I avoid this loop or make it faster?, R News 8(1), 46–50.
Lindley, D. V. (1983), Theory and practice of Bayesian statistics, The Statistician 32, 1–11. Litterman, R. & Scheikman, J. (1991), Common factors affecting bond returns, Journal of
Fixed Income 1, 54–61.
Liu, Y.-H., Makov, U. E. & Smith, A. F. M. (1996), Bayesian methods in actuarial science,
The Statistician 45, 503–515.
Loader, C. R. (1999), Local Regression and Likelihood, Statistics and Computing Series, Springer Verlag, New York.
Loecher, M. (2013), RgoogleMaps: Overlays on Google Map Tiles in R. R package version
1.2.0.3.
Lumley, T. (2004), Programmers’ niche: A simple class, in S3 and S4, R News 4(1), 33–36. Lunn, D., Spiegelhalter, D., Thomas, A. & Best., N. (2009), The BUGS project: Evolution,
critique and future directions (with discussion), Statistics in Medicine 28, 3049–3082.
Lyons, G., Forster, W., Kedney, P., Warren, R. & Wilkinson, H. (2002), Claims Reserving
Working Party paper, Institute of Actuaries, London.
MacAdie, C. J., Landsea, C. W., Neumann, C. J., David, J. E., Blake, E. & Hammer, G. R. (2009), Tropical Cyclones of the North Atlantic Ocean, 1851–2006, Technical memo, National Climatic Data Center in cooperation with the National Hurricane Center.
Mack, T. (1991), A simple parametric model for rating automobile insurance or estimating
IBNR claims reserves, ASTIN Bulletin 21, 93–109.
Mack, T. (1993), Distribution-free calculation of the standard error of chain ladder reserve estimates, ASTIN Bulletin 23, 213–225.
Mack, T. (1999), The standard error of chain ladder reserve estimates: Recursive calculation and inclusion of a tail factor, ASTIN Bulletin 29(2), 361–266.
Maindonald, J. & Braun, W. J. (2007), Data Analysis and Graphics Using R: An Example- Based Approach, Cambridge University Press, Berlin.
Markowitz, H. (1952), Portfolio selection, The Journal of Finance 7(1), 77–91.
Marshall, A. W. & Olkin, I. (1988), Families of multivariate distributions, Journal of the
American Statistical Association 83(403), 834–841.
Martinussen, T. & Scheike, T. (2006), Dynamic Regression Models for Survival Data, Springer, New York.
Massey, R., Widdows, J., Bhattacharya, K., Shaw, R., Hart, D., Law, D. & Hawes, W. (2002), Insurance company failure. General Insurance Convention Working Party. http://www.actuaries.org.uk/research-and-resources/documents/insurance-company- failure
Matloff, N. (2011), The Art of R Programming , No Starch Press, San Francisco.
May, E. (2004), Credit Scoring for Risk Managers: The Handbook for Lenders, South- Western Publishing, Independence, KY.
McCullagh, P. & Nelder, J. A. (1989), Generalized Linear Models (Second edition), Chapman
& Hall, London.
McGrayne, S. (2012), The Theory That Would Not Die, Yale University Press, New Haven, CT.
McNeil, A. (1997), Estimating the tails of loss severity distributions using extreme value theory, ASTIN Bulletin 27(1), 117–137.
McNeil, A. J. & Frey, R. (2000), Estimation of tail-related risk measures for heteroscedastic financial time series: An extreme value approach, Journal of Empirical Finance 7, 271–
300.
Merz, M. & Wu¨thrich, M. V. (2008a), Modelling the claims development result for solvency purposes, CAS E-Forum pp. 542–568.
Merz, M. & Wu¨thrich, M. V. (2008b), Prediction error of the multivariate chain ladder reserving method, North American Actuarial Journal 12, 175–197.
Metropolis, N. & Ulam, S. (1949), The Monte Carlo method, Journal of the American
Statistical Association 44(247), 335–341.
Meucci, A. (2009), Risk and Asset Allocation, Springer Verlag, Berlin.
Meyers, G. & Cummings, D. (2009), “Goodness of fit” vs. “goodness of lift”, Actuarial
Review 36, 16–17.
Michaels, D. (2002), APH: How the Love Carnal and silicone implants nearly destroyed Lloyd’s (slides)’, http://www.actuaries.org.uk/research-and-resources/documents/ aph-how-love-carnal-and-silicone-implants-nearly-destroyed-lloyds-s. Presented at the Younger Members’ Convention.
Miranda, M. D. M., Nielsen, J. P. & Verrall, R. (2012), Double Chain Ladder, ASTIN Bulletin, 42(1), P. 59-76.
Moler, C. & Van Loan, C. (1978), Nineteen dubious ways to compute the matrix exponential,
SIAM Review 20(4), 801–836.
Moran, P. (1971), Maximum-likelihood estimation in non-standard conditions, Mathematical
Proceedings of the Cambridge Philosophical Society 70(3), 441–450. Murdoch, D. (2002), Reading foreign files, R News 2(1), 2–3.
Murell, P. (2012), R Graphics, Chapman & Hall / CRC Press, Boca Raton, FL. Murphy, D. (1994), Unbiased loss development factors, PCAS 81, 154 – 222.
Murray, R. & Lauder, A. (2011), Modelling the Claims Process — An Alternative to Devel- opment Factor Modelling, Institute of Actuaries. GIRO Conference and Exhibition.
Murrell, P. & Ripley, B. (2006), Non-standard fonts in PostScript and PDF graphics, R News 6(2), 41–47.
Nelsen, R. B. (2006), An Introduction to Copulas, Springer, New York.
Nelson, C. & Siegel, A. (1987), Parsimonious modelling of yield curves, Journal of Business
60.
Niederreiter, H. (1992), Random Number Generation and Quasi-Monte-Carlo Methods, Society for Industrial and Applied Mathematics, Philadelphia.
Nisbet, R., Elder, J. & Miner, G. (2011), Handbook of Statistical Analysis and Data Mining
Applications, Academic Press, New York.
Norberg, R. (1976), A credibility theory for automobile bonus systems, Scandinavian Actuarial Journal pp. 92–107.
Norberg, R. (1979), The credibility approach to ratemaking, Scandinavian Actuarial Journal
1979, 181–221.
Norris, J. (1997), Markov Chains, Cambridge University Press, New York.
Novomestky, F. (2012), rportfolios: Random Portfolio Generation. R package version
1.0.
O’Cinneide, C. (1990), Characterization of phase-type distributions, Stochastic Models
6(1), 1–57.
Ohlsson, E. & Johansson, B. (2010), Non-Life Insurance Pricing with Generalized Linear
Models, Springer Verlag, Berlin.
Ohlsson, E. & Lauzeningks, J. (2009), The one-year non-life insurance risk, Insurance: Mathematics and Economics 45(2), 203–208.
Olver, F. W. J., Lozier, D. W., Boisvert, R. F. & Clark, C. W., Eds. (2010), NIST Handbook of Mathematical Functions, Cambridge University Press, New York.
Orr, J. (2007), A Simple Multi-State Reserving Model, Colloqiua Orlando ed., ASTIN Colloquium in Orlando.
Orr, J. (2012), GIROC Reserving Research Workstream, Institute of Actuaries, London. Pagan, A. (1996), The econometrics of financial markets, Journal of Empirical Finance
3(1), 15–102.
Palm, F. C. (1996), 7 GARCH models of volatility, Handbook of Statistics 14, 209–240. Panjer, H. H. (1981), Recursive evaluation of a family of compound distributions, ASTIN
Bulletin 12, 22–26.
Pasupathy, R. (2010), Generating nonhomogeneous Poisson processes, in Wiley Encyclopedia of Operations Research and Management Science, Wiley & Sons, New York.
Pearson, K. (1895), Contributions to the mathematical theory of evolution, II: Skew variation in homogeneous material, Philosophical Transactions of the Royal Society of London.
186, 343–414.
Pfaff, B. (2012), Financial Risk Modelling and Portfolio Optimization with R, John Wiley
& Sons, Ltd., New York.
Pickle, L. W., Mungiole, M., Jones, G. K. & White, A. A. (1999), Exploring spatial patterns of mortality: The new atlas of United States mortality, Statistics in Medicine
18(23), 3211–3220.
Pielke, J. R. A., Gratz, J., Landsea, C. W., Collins, D., Saunders, M. A. & Musulin, R. (2008), Normalized hurricane damages in the United States: 1900–2005, Natural Hazards Review 9, 29–42.
Pinheiro, J. C. & Bates, D. M. (2000), Mixed-Effects Models in S and S-Plus, 1 ed., Springer- Verlag, Berlin.
Planchet, F. & Th´erond, P. (2011), Mod´elisation Statistique des Ph´enom`enes de Dur´ee— Applications Actuarielles, Assurance Audit Actuariat, Economica Paris.
Plat, R. (2009), On stochastic mortality modeling, Insurance: Mathematics and Economics
45(3), 393–404.
Plummer, M. (2003), JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling, in Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria.
Plummer, M. (2011), JAGS Version 3.1.0 User Manual.
Plummer, M. (2013), rjags: Bayesian Graphical Models Using MCMC. R package version
3-10.
Press, W., Teukolsky, S., Vetterling, W. & Flannery, B. (2007), Numerical Recipes: The Art of Scientific Computing, Cambridge University Press, London.
Pr¨ohl, C. & Schmidt, K. D. (2005), Multivariate chain-ladder, Dresdner Schriften zur Versicherungsmathematik .
Quarg, G. & Mack, T. (2004), Munich chain ladder, Munich Re Group. Casualty Actuarial
Society, Vol. 2, Issue 2, 266–299.
Quenouille, M. H. (1949), Approximate tests of correlation in time series, Journal of the
Royal Statistical Society Series B 11, 68–84.
Raftery, A. E., Chunn, J. L., Gerland, P. & Sevˇc´ıkov´a, H. (2013), Bayesian probabilistic projections of life expectancy for all countries, Demography 50, 777–801.
Ramsay, J. O. & Silverman, B. W. (2005), Functional Data Analysis, 2nd ed., Springer- Verlag, New York.
Rebonato, R., Sukhdeep, M., Mark, J., Lars-Dierk, B. & Ken, N. (2005), Evolving yield curves in the real-world measures: A semi-parametric approach, The Journal of Risk
7(3), 29–61.
Renshaw, A. E. & Haberman, S. (2003), Lee-Carter mortality forecasting with age-specific enhancement, Insurance: Mathematics and Economics 33(2), 255–272.
Renshaw, A. E. & Haberman, S. (2006), A cohort-based extension to the Lee–Carter model for mortality reduction factors, Insurance: Mathematics and Economics 38(3), 556–570.
Renshaw, A. E. & Verrall, R. J. (1998), A stochastic model underlying the chain-ladder technique, British Actuarial Journal 4, 903–923.
Ripley, B. D. (2001), Installing R under Windows, R News 1(2), 11–14.
Ripley, B. D. (2005), Packages and their management in R 2.1.0, R News 5(1), 8–11. Ripley, B. D. & Hornik, K. (2001), Date-time classes, R News 1(2), 8–11.
Robert, C. P. & Casella, G. (2010), Introducing Monte Carlo Methods with R, Springer, New York.
Rolski, T., Schmidli, H., Schmidt, V. & Teugels, J. (1999), Stochastic Processes for Insurance and Finance, Wiley, Chichester.
Ross, S. (2009), First Course in Probability, A (8th Edition), 8 ed., Prentice Hall, Englewood
Cliffs, NJ.
Rousseeuw, P., Croux, C., Todorov, V., Ruckstuhl, A., Salibian-Barrera, M., Verbeke, T., Koller, M. & Maechler, M. (2012), robustbase: Basic Robust Statistics. R package version
0.9-7.
Rousseeuw, P. J. & Driessen, K. V. (1999), A fast algorithm for the minimum covariance determinant estimator, Technometrics (41), 212–223.
Rowe, B. L. Y. (2013), tawny: Provides Various Portfolio Optimization Strategies Including
Random Matrix Theory and Shrinkage Estimators. R package version 2.1.0.
Royston, R., Altman, D. & Sauerbrei, W. (2006), Dichotomizing continuous predictors in multiple regression: a bad idea, Statistics in Medicine 25(1), 49–56.
Rubin, D. (1976), Inference and missing data, Biometrika 63, 581–592.
Ruckdeschel, P., Kohl, M., Stabla, T., & Camphausen, F. (2006), S4 classes for distributions,
R News 6(2), 2–6.
Ruckman, C. & Francis, J. (n.d.), Financial Mathematics: A Practical Guide for Actuaries and Other Business Professionals, BPP Professional Education, Phoenix, AZ.
Rue, H., Martino, S. & Chopin, N. (2009), Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations, Journal of the Royal Statistical Society: Series B (Statistical Methodology) 71(2), 319–392.
Santos Silva, J. & Windmeijer, F. (2001), Two-part multiple spell models for health care demand, Journal of Econometrics 104(1), 67–89,.
Sarkar, D. (2002), Lattice, R News 2(2), 19–23.
Sarkar, D. (2008), Lattice: Multivariate Data Visualization with R, Springer Verlag, Berlin. Satchell, S. & Scowcroft, A. (2000), A demystification of the Black-Litterman model: Man-
aging quantitative and traditional portfolio construction, Journal of Asset Management
1, 138–150.
Scarsini, M. (1984), On measures of concordance, Stochastica 8, 201–218.
Schmidberger, M., Morgan, M., Eddelbuettel, D., Yu, H., Tierney, L. & Mansmann, U. (2009), State of the art in parallel computing with R, Journal of Statistical Software
31(1), 1–27.
Schmidt, K. D. (2012), A bibliography on loss reserving, http://www.math.tudresden.de/sto/schmidt/dsvm/reserve.pdf.
Schmock, U. (1999), Estimating the value of the WINCAT coupons of the Winterthur insurance convertible bond, ASTIN Bulletin 29(1), 101–163.
Shang, H. L., Booth, H. & Hyndman, R. J. (2011), Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods, Demographic Research 25(5), 173–214.
Shanno, D. F. (1970), Conditioning of quasi-newton methods for function minimization,
Mathematics of Computation 24, 647–656.
Shao, J. & Tu, D. (1995), The Jackknife and Bootstrap, Springer, New York.
Silverman, B. (1986), Density Estimation for Statistics and Data Analysis, Chapman & Hall, London.
Sklar, A. (1959), Fonctions de r´epartition `a n dimensions et leurs marges, Publications de l’ISUP de Paris 8 8, 229–231.
Smyth, G. & Jørgensen, B. (2002), Fitting Tweedie’s Compound Poisson Model to Insurance
Claims Data: Dispersion Modelling, ASTIN Bulletin 32, 143–157.
Sobol, I. (1967), On the distribution of points in a cube and the approximate evaluation of integrals, USSR Computational Mathematics and Mathematical Physics 7, 86–112.
Spector, P. (2008), Data Manipulation with R, Springer Verlag, Berlin.
Spedicato, G. A. (2013a), Lifecontingencies: An R package to perform life contingencies actuarial mathematics. R package version 0.9.8.
Spedicato, G. A. (2013b), markovchain: An R package to easily handle discrete Markov chains. R package version 0.0.2.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Linde, A. (2002), Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society, Series B 64, 583–639.
Stan Development Team (2012), Stan: A C++ Library for Probability and Sampling, version
1.0.
Steehouwer, H. (2005), Macroeconomic Scenarios and Reality: A Frequency Domain Approach for Analyzing Historical Time Series and Generating Scenarios for the Future, PhD thesis, Free University of Amsterdam.
Stroustrup, B. (2013), The C/C++ Programming Language, Addison-Wesley.
Sturtz, S., Ligges, U. & Gelman, A. (2005), R2winbugs: A package for running winbugs from R, Journal of Statistical Software 12(3), 1–16.
Svensson, L. (1994), Estimating and Interpreting Forward Interest Rates: Sweden 1992–
1994, International Monetary Fund, Washington, D.C.
Teetor, P. (2011), R Cookbook, O’Reilly Media, Sebastopol, CA.
Teugels, J. (1982), Large claims in insurance mathematics, ASTIN Bulletin 13, 81–88. Theussl, S. (2013), Optimization and Mathematical Programming. CRAN Task View. Thomas, A., O’Hara, B., Ligges, U. & Sturtz, S. (2006), Making bugs open, R News 6(1), 12–
17.
Thomas, L. (2000), A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers, International Journal of Forecasting 16, 149–172.
Tibshirani, R. (1996), Regression shrinkage and selection via the lasso, Journal of the Royal
Statistical Society B 58(1), 267–288.
Tomas, J. (2011), A local likelihood approach to univariate graduation of mortality, Bulletin
Fran¸cais d’Actuariat 11(22), 105–153.
Tomas, J. (2013), Quantifying Biometric Life Insurance Risks with Non-Parametric Methods, PhD thesis, Amsterdam School of Economics Research Institute.
Tomas, J. & Planchet, F. (2014), Constructing entity specific prospective mortality table: Adjustment to a reference, European Actvarial Journal to appear.
Tomas, J. & Planchet, F. (2013), Multidimensional smoothing by adaptive local kernel- weighted log-likelihood with application to long-term care insurance, Insurance: Mathematics & Economics 52(3), 573–589.
Tse, Y. (2009), Nonlife Actuarial Models: Theory, Methods and Evaluation, International
Series on Actuarial Science, Cambridge University Press, London. Tuff´ery, S. (2011), Data Mining and Statistics for Decision Making, Wiley.
Tukey, J. W. (1958), Bias and confidence in not quite large samples (Abstract), Annual of
Mathematical; Statistics 29, 614.
Tuljapurkar, S., Li, N. & Boe, C. (2000), A universal pattern of mortality decline in the G7 countries, Nature 405, 789–792.
Uryasev, S. & Rockafellar, R. T. (2001), Conditional value-at-risk: Optimization approach, in Stochastic Optimization: Algorithms and Applications, Springer Verlag, Berlin, pp. 411–
435.
Van den Broek, J. (1995), A score test for zero inflation in a Poisson distribution, Biometrics
51, 738–743.
Vance, A. (2009), Data analysts captivated by R’s power, New York Times (January 7th), B6. http://www.newyorktimes.com/2009/01/07/technology/business- computing/07program.html
Vasicek, O. (1977), An equilibrium characterization of the term structure, Journal of Financial Economics 5, 177–188.
Vaupel, J., Manton, K. & Stallard, E. (1979), The impact of heterogeneity in individual frailty on the dynamics of mortality, Demography 16, 439–454.
Venables, W. N. & Ripley, B. D. (2002), Modern Applied Statistics with S, 4th ed., Springer, New York.
Venzon, D. J. & Moolgavkar, S. H. (1988), A method for computing profile-likelihood-based confidence intervals, Applied Statistics 37(1), 87–94.
Verbeke, G. & Molenberghs, G. (2000), Linear Mixed Models for Longitudinal Data, Springer
Series in Statistics, Springer, Berlin.
von Neumann, J. (1951), Various techniques used in connection with random digits, National
Bureau of Standards, Applied Mathematics Series 12, 36–38.
Wainer, H. (2006), Finding what is not there through the unfortunate binning of results: The Mendel effect, Chance 19, 127–141.
Weingessel, A. (2011), quadprog: Functions to Solve Quadratic Programming Problems. R
package version 1.5-4; S original by Berwin A. Turlach.
Wei, X. (2012), PROC R: A SAS macro that enables native R programming in the base SAS
environment, Journal of Statistical Software, Code Snippets 46(2), 1–13. Whittle, P. (1954), On stationary processes in the plane, Biometrika 41, 434–439. Wickham, H. (2009), ggplot2: Elegant Graphics for Data Analysis, Springer Verlag, Berlin.
Wickham, H. (2011), The split-apply-combine strategy for data analysis, Journal of Statistical Software 40(1), 1–29.
Wilkinson, G. N. & Rogers, C. E. (1977), Symbolic description of factorial models for analysis of variance, Journal of the Royal Statistical Society. Series C (Applied Statistics)
22, 392–399.
Wilkinson, J. H. (1963), Rounding Errors in Algebraic Processes, Prentice-Hall, Englewood
Cliffs, NJ.
Wilkinson, L. (1999), The Grammar of Graphics, Springer Verlag, Berlin.
Willets, R. C. (2004), The cohort effect: insights and explanations, Cambridge Univversity
Press, New York.
Williamson, R. (1989), Probabilistic Arithmetic. PhD thesis, University of Queensland.
Wood, S. N. (1994), Monotonic smoothing splines fitted by cross validation, SIAM Journal on Scientific Computing 15(5), 1126–1133.
Wood, S. N. (2003), Thin plate regression splines, Journal of the Royal Statistical Society, Series B 65(1), 95–114.
Wood, S.N. (2006), Generalized Additive Models: An Introduction with R, Chapman and
Hall/CRC Press, Boca Raton, FL, London.
Wooldridge, J. (2010), Econometric Analysis of Cross Section and Panel Data, 2nd ed., The
MIT Press, Cambridge, MA.
Wu¨rtz, D., Chalabi, Y., Chen, W. & Ellis, A. (2009), Portfolio Optimization with R/Rmetrics, Finance Online GmbH, Zurich.
Wu¨therich, M. V. & Merz, M. (2008), Stochastic Claims Reserving Methods in Insurance, Wiley Finance, New York.
Yan, J. & Prates, M. (2012), rbugs: Fusing R and OpenBugs. R package version 0.5-6. Yang, R. & Berger, J. O. (1998), A catalog of noninformative priors. working paper, Duke
University, Durham, NC.
Ye, Y. (1987), Interior Algorithms for Linear, Quadratic, and Linearly Constrained Non- Linear Programming, PhD thesis, Department of EES, Stanford University.
Yee, T. W. (2008), The VGAM package, R News 8(2), 28–39.
Yu, H. (2002), Rmpi: Parallel statistical computing in R, R News 2(2), 10–14.
Zehnwirth, B. (1994), Probabilistic development factor models with applications to loss reserve variability, prediction intervals and risk based capital, Casualty Actuarial Society Forum 2, 447–605.
Zeileis, A. (2005), ‘CRAN task views’, R News 5(1), 39–40.
Zeileis, A., Kleiber, C. & Jackman, S. (2008), Regression models for count data in R, Journal of Statistical Software 27, 1–25.
Zhang, J. & Gentleman, R. (2004), Tools for interactively exploring R packages, R News
4(1), 20–25.
Zhang, Y. (2010), A general multivariate chain ladder model, Insurance: Mathematics and
Economics 46, 588–599.
Zhang, Y., Dukic, V. & Guszcza, J. (2012), A Bayesian nonlinear model for forecasting insurance loss payments, Journal of the Royal Statistical Society, Series A 175, 637–656.
Zhu, C., Byrd, R. H., Lu, P. & Nocedal, J. (1997), Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization, ACM Transactions on Mathematical Software (TOMS) 23(4), 550–560.
Zuur, A. F., Ieno, E. N. & Meesters, E. (2009), A Beginner’s Guide to R, Springer Verlag, Berlin.
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