Statistical inference for generalized additive partially linear models

JOURNAL OF MULTIVARIATE ANALYSIS

卷:162

Article

Liu, Rong¹  Haerdie, Wolfgang K.^2,3  Zhang, Guoyi⁴ 

[1] Univ Toledo, Dept Math & Stat, 2801 W Bancroft St, Toledo, OH 43606 USA

[2] Humboldt Univ, Ctr Appl Stat & Econ, Berlin, Germany

[3] Singapore Management Univ, Sch Business, Singapore, Singapore

[4] Univ New Mexico, Dept Math & Stat, Albuquerque, NM 87131 USA

关键词: B-spline;    Default;    Empirical likelihood;    Kernel estimator;    Link function;    Mixing;   

DOI  :  10.1016/j.jmva.2017.07.011

来源: Elsevier

【 摘 要 】

The class of Generalized Additive Models (GAMs) is a powerful tool which has been well studied. It helps to identify additive regression structure that can be determined even more sharply via test procedures when some component functions have a parametric form. Generalized Additive Partially Linear Models (GAPLMs) enjoy the simplicity of GLMs and the flexibility of GAMs because they combine both parametric and nonparametric components. We use the hybrid spline-backfitted kernel estimation method, which combines the best features of both spline and kernel methods, to make fast, efficient and reliable estimation under an alpha-mixing condition. In addition, simultaneous confidence corridors (SCCs) for testing overall trends and empirical likelihood confidence regions for parameters are provided under an independence condition. The asymptotic properties are obtained and simulation results support the theoretical properties. As an illustration, we use GAPLM methodology to improve the accuracy ratio of the default predictions for 19,610 German companies. The quantlet for this paper are available on https://github.com. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free