426 Handbook of Big Data
44. S. Sapp, M.J. van der Laan, and J. Canny. Subsemble: An ensemble method for
combining subset-specific algorithm fits. Journal of Applied Statistics, 41(6):1247–1259,
2014.
45. S. Sapp, M.J. van der Laan, and K. Page. Targeted estimation of binary variable import-
ance measures with interval-censored outcomes. Int J Biostat, 10(1):77–97, 2014.
46. J. Schwab, S. Lendle, M. Petersen, and M. van der Laan. ltmle: Longitudinal Targeted
Maximum Likelihood Estimation. R package version 0.9.3, 2013.
47. O.M. Stitelman and M.J. van der Laan. Collaborative targeted maximum likelihood for
time-to-event data. Int J Biostat, 6(1):Article 21, 2010.
48. O.M. Stitelman and M.J. van der Laan. Targeted maximum likelihood estimation of
effect modification parameters in survival analysis. Int J Biostat, 7(1), 2011.
49. C. Tuglus and M.J. van der Laan. Targeted methods for biomarker discovery. In M.J. van
der Laan and S. Rose, editors, Targeted Learning: Causal Inference for Observational
and Experimental Data, pp. 367–382. Springer, Berlin, Germany, 2011.
50. M.J. van der Laan. Estimation based on case-control designs with known prevalence
probability. Int J Biostat, 4(1):Article 17, 2008.
51. M.J. van der Laan. Estimation of causal effects of community-based interventions.
Technical Report 268, Division of Biostatistics, University of California, Berkeley, CA,
2010.
52. M.J. van der Laan. Targeted maximum likelihood based causal inference: Part I. Int J
Biostat, 6(2):Article 2, 2010.
53. M.J. van der Laan. Causal inference for networks. J Causal Inference, 2(1):13–74, 2014.
54. M.J. van der Laan and S. Dudoit. Unified cross-validation methodology for selection
among estimators and a general cross-validated adaptive epsilon-net estimator: Finite
sample oracle inequalities and examples. Technical Report 130, Division of Biostatistics,
University of California, Berkeley, CA, 2003.
55. M.J. van der Laan and S. Gruber. Collaborative double robust penalized targeted
maximum likelihood estimation. Int J Biostat, 6(1):Article 17, 2010.
56. M.J. van der Laan and S. Gruber. Targeted minimum loss based estimation of causal
effects of multiple time point interventions. Int J Biostat, 8(1), 2012.
57. M.J. van der Laan and S. Lendle. Online targeted learning. Technical Report 330,
Division of Biostatistics, University of California, Berkeley, CA, 2014.
58. M.J. van der Laan, E.C. Polley, and A.E. Hubbard. Super learner. Stat Appl Genet Mol,
6(1):Article 25, 2007.
59. M.J. van der Laan and J.M. Robins. Unified Methods for Censored Longitudinal Data
and Causality. Springer, Berlin, Germany, 2003.
60. M.J. van der Laan and S. Rose. Statistics ready for a revolution: Next generation of
statisticians must build tools for massive data sets. Amstat News, 399:38–39, 2010.
61. M.J. van der Laan and S. Rose. Targeted Learning: Causal Inference for Observational
and Experimental Data. Springer, Berlin, Germany, 2011.
Targeted Learning for Variable Importance 427
62. M.J. van der Laan and D.B. Rubin. Targeted maximum likelihood learning. Int J
Biostat, 2(1):Article 11, 2006.
63. H. Wang, S. Rose, and M.J. van der Laan. Finding quantitative trait loci genes. In
M.J. van der Laan and S. Rose, editors, Targeted Learning: Causal Inference for
Observational and Experimental Data, pp. 383–394. Springer, Berlin, Germany, 2011.
64. H. Wang, S. Rose, and M.J. van der Laan. Finding quantitative trait loci genes with
collaborative targeted maximum likelihood learning. Stat Prob Lett, 81(7):792–796,
2011.
65. H. Wang and M.J. van der Laan. Dimension reduction with gene expression data using
targeted variable importance measurement. BMC Bioinformatics, 12(1):312, 2011.
66. H. Wang, Z. Zhang, S. Rose, and M.J. van der Laan. A novel targeted learning methods
for quantitative trait loci mapping. Genetics, 198(4):1369–1376, 2014.
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