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The Basic Ideas
An Outline of General Methods
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A(Ge analysis of variance applied approximating family approximating model assume assumption asymptotic criteria autoregressive Bootstrap methods chapter chi-squared discrepancy components computed consider corresponding crepancy cross-validation Cross-validatory criterion denote derived discrepancy due distribution function due to approximation due to estimation eijk empirical discrepancy estimator of 9 example expected discrepancy family of models Figure Fourier series frequency gamma Gauss discrepancy ge(x histogram hypothesis identically distributed intervals leads least-squares estimator Lemma linear Linhart logit lognormal mating models matrix maximum likelihood estimator mean squared mean squared error minimum discrepancy estimator model selection negative binomial normally distributed number of parameters observations obtained operating family operating model orthogonal polynomial predictor variables probability procedure random variables regression analysis residual process sample saturated model Section selected model simple simpler criterion spectral density standard deviation Statistical sums of squares Theorem trace term unbiased estimator vector zero Zucchini