Likelihood inferences for the link function without knowing the true underlying distributions

Research output: Contribution to journalArticlepeer-review

Abstract

This article is concerned with inference about link function in generalized linear models. Aparametric and yet robust likelihood approach is introduced to accomplish the intended goal.More specifically, it is demonstrated that one can convert normal and gamma likelihoods into robust likelihood functions for the link function. The asymptotic validity of the robust likelihood requires only the existence of the second moments of the underlying distributions. The application of this novel robust likelihood method is demonstrated on the Box-Cox transformation. Simulation studies and real data analysis are provided to demonstrate the efficacy of the new parametric robust procedures.

Original languageEnglish
Pages (from-to)507-519
Number of pages13
JournalComputational Statistics
Volume26
Issue number3
DOIs
StatePublished - Sep 2011

Keywords

  • Box-Cox transformation
  • Link function
  • Robust likelihood

Fingerprint

Dive into the research topics of 'Likelihood inferences for the link function without knowing the true underlying distributions'. Together they form a unique fingerprint.

Cite this