Premium
Model diagnostics for smoothing spline ANOVA models
Author(s) -
Gu Chong
Publication year - 2004
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3316020
Subject(s) - interpretability , smoothing spline , smoothing , spline (mechanical) , statistics , mathematics , econometrics , computer science , estimation , variance (accounting) , artificial intelligence , spline interpolation , engineering , accounting , structural engineering , systems engineering , business , bilinear interpolation
Abstract The author proposes some simple diagnostics for assessing the necessity of selected terms in smoothing spline ANOVA models. The elimination of practically insignificant terms generally enhances the interpretability of the estimates and sometimes may also have inferential implications. The diagnostics are derived from Kullback‐Leibler geometry and are illustrated in the settings of regression, probability density estimation, and hazard rate estimation.