Statistics, Volume 6, Part 2Emlyn 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 combined order statistic 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 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 S₂ scaling sequential solution span spectrum SPRT sum of squares Suppose symmetric Theorem v₁ variance w₁ x₁ y₁ zero