Commonly Used Notation
| In | The n by n identity matrix |
| 1n | The n by 1 vector of unit elements |
| O | A matrix of appropriate order with all zero entries |
| Λi | The ith largest eigenvalue of the matrix under consideration |
| |A| | The determinant of the square matrix A |
| tr(A) | The trace of the square matrix A |
| A−1 | The inverse of the matrix A |
| A1/2 | The symmetric square root of the matrix A |
| A− | A generalized inverse of the matrix A |
| E(y) | Expected value of a random variable or vector y |
| v(y), var(y) | Variance of a random variable y |
| cov(x, y) | Covariance of random variable (vector) x with random variable (or vector) y |
| D(y) | The variance covariance or the dispersion matrix of y |
| Np (μ, Σ) | A p-dimensional normal distribution with mean μ and the variance covariance matrix Σ |
| Wp (ƒ, Σ) | A p-(matrix) variate Wishart distribution with ƒ degrees of freedom and parameter Σ (that is, with expected value ƒ Σ) |
| ε | Error vector |
| ϵ | Error matrix |
| Y | n by p matrix of data on dependent variables |
| X | Regression/Design matrix in the linear model |
| β | Regression/Design parameter vector |
| B | Regression/Design parameter matrix |
| Σ | (usually) The Dispersion matrix of errors |
| df | Degrees of freedom |
| SS&CP Matrix | Matrix of the sums of squares and crossproducts |
| E | Error SS&CP matrix |
| H | Hypothesis SS&CP matrix |
 | The sample mean vector |
| S | Sample dispersion matrix (with d f as denominator) |
| Sn | Sample dispersion matrix (with sample size as denominator) |
| P | The Projection or Hat matrix |
| T2 | Hotelling's T2 |
| Λ | Wilks' Lambda |
| β1,p | Coefficient of multivariate skewness |
| β1,p | Coefficient of multivariate kurtosis |
| ⊗ | Kronecker product |
| AIC | Akaike's information criterion |
| BIC | Swartz's Bayesian information criterion |
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.