Variable Selection for Spatial Logistic Autoregressive Models

Autor:	Jiaxuan Liang, Yi Cheng, Yuqi Su, Shuyue Xiao, Yunquan Song
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	spatial logistic autoregressive model variable selection maximum likelihood Mathematics QA1-939
Zdroj:	Mathematics, Vol 10, Iss 17, p 3095 (2022)
Druh dokumentu:	article
ISSN:	2227-7390
DOI:	10.3390/math10173095
Popis:	When the spatial response variables are discrete, the spatial logistic autoregressive model adds an additional network structure to the ordinary logistic regression model to improve the classification accuracy. With the emergence of high-dimensional data in various fields, sparse spatial logistic regression models have attracted a great deal of interest from researchers. For the high-dimensional spatial logistic autoregressive model, in this paper, we propose a variable selection method with for the spatial logistic model. To identify important variables and make predictions, one efficient algorithm is employed to solve the penalized likelihood function. Simulations and a real example show that our methods perform well in a limited sample.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/7e02526950cb4cad8f89aacd5b3969c0 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.