L2 differentiability of generalized linear models

Autor:	Peter Ruckdeschel, Daria Pupashenko, Matthias Kohl
Rok vydání:	2015
Předmět:	Statistics and Probability Generalized linear model Smoothness (probability theory) 05 social sciences Mathematical analysis Generalized linear array model 01 natural sciences Generalized linear mixed model Hierarchical generalized linear model 010104 statistics & probability Exponential family 0502 economics and business Applied mathematics 0101 mathematics Statistics Probability and Uncertainty Semi-differentiability Generalized additive model for location scale and shape 050205 econometrics Mathematics
Zdroj:	Statistics & Probability Letters. 97:155-164
ISSN:	0167-7152
DOI:	10.1016/j.spl.2014.11.020
Popis:	We derive conditions for L 2 differentiability of generalized linear models with error distributions not necessarily belonging to exponential families, covering both cases of stochastic and deterministic regressors. These conditions induce smoothness and integrability conditions for corresponding GLM-based time series models.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c068b723b4b3d6c7b2d5d8634821d3a7 https://doi.org/10.1016/j.spl.2014.11.020 Zobrazit plný text záznamu Full Text from ScienceDirect