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LINEAR MODELS I
LINEAR MODELS II
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algorithm analysis approximation assumed Bayes Bayesian Chapter coefficients columns combined order statistic consider coordinates correlation corresponding covariance matrix credible interval decision defined degrees of freedom denote derived deviance distances eigenvalues eigenvectors empirical distribution function error estimate example expected explanatory variables exponential family extended Kalman filter factor fitted values forecast frequency given gives hypothesis independent indicator vectors interval Kalman filter least squares linear combination linear models linear predictor mathematical measurement median methods minimax minimize Normal distribution null hypothesis observations obtained OnOnOnOnOn OnOnOnOnOn OnOnOnOnOn ooooo ooooo order statistic orthogonal orthogonal matrix parameters periodogram points population posterior density principal components prior beliefs probability problem procedure Procrustes Procrustes analysis rainfall random sample random variable rank regression residual result risk functions rows scaling sequential solution spectrum SPRT sum of squares Suppose symmetric symmetric matrix Theorem variance weight zero