Assessing Influence on Partially Varying-coefficient Generalized Linear Model

Autor:	Germán Ibacache-Pulgar, Valeria Lira, Cristian Villegas
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	exponential family maximum penalized likelihood estimators likelihood displacement semiparametric models weighted back-fitting algorithm Statistics HA1-4737 Probabilities. Mathematical statistics QA273-280
Zdroj:	Revstat Statistical Journal, Vol 22, Iss 3 (2024)
Druh dokumentu:	article
ISSN:	1645-6726 2183-0371
DOI:	10.57805/revstat.v22i3.507
Popis:	In this paper we discuss estimation and diagnostic procedures in partially varying-coefficient generalized linear models based in the penalized likelihood function. Specifically, we derive a weighted back-fitting algorithm to estimate the model parameters using smoothing spline. Moreover, we developed the local influence method to assess the sensitivity of maximum penalized likelihood estimators when small perturbations are introduced into the model or data. Finally, an example with real data of ozone concentration is given for illustration.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/4200b5ac962e4c9e81aed45ad8eca13c Zobrazit plný text záznamu View record in DOAJ