Handbook of Applicable Mathematics, StatisticsWalter Ledermann, Emlyn Lloyd |
Contents
INTRODUCTION XV | 493 |
LINEAR MODELS I | 499 |
LINEAR MODELS II | 549 |
Copyright | |
10 other sections not shown
Common terms and phrases
a₁ a₂ algorithm analysis approximation assumed b₁ b₂ Bayes Bayesian C₁ coefficients columns consider coordinates correlation corresponding covariance matrix credible interval d₁ d₂ decision defined denote derived deviance distances eigenvalues eigenvectors empirical distribution function error estimate example expected explanatory variables extended Kalman filter factor fitted values forecast frequency given gives H₁ hypothesis independent interval Kalman filter least squares likelihood linear models linear predictor m₁ mathematical measurement methods minimax minimize n₁ n₂ Normal distribution null hypothesis observations obtained order statistic orthogonal orthogonal matrix P₁ parameters periodogram points population posterior density principal components prior beliefs probability problem procedure Procrustes Procrustes analysis random sample random variable rank regression residual result rows scaling sequential solution span spectrum SPRT sum of squares Suppose symmetric Theorem u₁ v₁ variance w₁ x₁ y₁ zero