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Background material and model description
Asymptotic properties of the MPLE
1 other sections not shown
ah(z algorithm asserted assumption asymptotic properties canonical link Chapter Classical linear models Condition ND constant n2 covariates cross-validation define definition denote dimensional DjvD Example exists 8(A exists a constant exponential family first-order Markov process GLM's go to Step Hence inequality j e J(Gj j e JN 2(f J(Sh JNi2 Lebesgue measurable likelihood principle linear combination linear models link family link function lnPe(Y log-linear model logistic model lt follows Markov process maximizer menarche misspecified MPLE multivariate normal distribution Nelder Newton-Raphson method non-parametric models notation obtain Op(N P(EN parametric and semi-parametric parametric models partition penalized likelihood penalty term predict projection pursuit Proof of Lemma Remark replace tp+1 Royal Statistical Society s e SN semi-parametric extension semi-parametric models Semi-parametric regression models simplex algorithm sJb1 SPGLM Stukel suffices to show Theorem vol(S Wahba zp+1