Use generalized linear models or generalized partially linear models?

被引：0

作者：

Li, Xinmin ^{[1
]}

Liang, Haozhe ^{[2
]}

Haerdle, Wolfgang ^{[3
]}

Liang, Hua ^{[4
]}

机构：

[1] Qingdao Univ, Sch Math & Stat, Qingdao 266071, Shandong, Peoples R China

[2] Univ Sci & Technol China, Dept Stat & Finance, Hefei 230026, Anhui, Peoples R China

[3] Humboldt Univ, Inst Stat & Okonometrie, D-10178 Berlin, Germany

[4] George Washington Univ, Dept Stat, Washington, DC 20052 USA

来源：

STATISTICS AND COMPUTING | 2023年 / 33卷 / 05期

关键词：

Backfitting; Generalized additive models; Goodness-of-fit; Penalized spline; Profile likelihood; Quasi-likelihood; LOGISTIC-REGRESSION;

D O I：

10.1007/s11222-023-10278-4

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We propose test statistics based on the penalized spline to decide between generalized linear models and generalized partially linear models. The numerical performance of the proposed statistics is comparable to that of their kernel-based competitors, which have been shown to be asymptotically normal in the literature (Hardle et al. in J Am Stat Assoc 93:1461-1474, 1998). We also numerically explore the possibility of using the proposed statistics for goodness of fit checking for GLM. The proposed proposed procedures are illustrated to analyze two datasets.

引用

页数：10

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