Analysis of Deviance for Hypothesis Testing in Generalized Partially Linear Models
Statistics Theory
2020-09-10 v1 Statistics Theory
Abstract
In this study, we develop nonparametric analysis of deviance tools for generalized partially linear models based on local polynomial fitting. Assuming a canonical link, we propose expressions for both local and global analysis of deviance, which admit an additivity property that reduces to analysis of variance decompositions in the Gaussian case. Chi-square tests based on integrated likelihood functions are proposed to formally test whether the nonparametric term is significant. Simulation results are shown to illustrate the proposed chi-square tests and to compare them with an existing procedure based on penalized splines. The methodology is applied to German Bundesbank Federal Reserve data.
Cite
@article{arxiv.2009.04252,
title = {Analysis of Deviance for Hypothesis Testing in Generalized Partially Linear Models},
author = {Wolfgang Karl Härdle and Li-Shan Huang},
journal= {arXiv preprint arXiv:2009.04252},
year = {2020}
}