Nonparametric estimation in generalized varying-coefficient models based on iterative weighted quasi-likelihood method

• Published in: Computational statistics Vol. 31; no. 1; pp. 247 - 268
• Main Authors: Zhao, Yan-Yong, Lin, Jin-Guan, Huang, Xing-Fang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2016; Springer Nature B.V
• Subjects: Asymptotic properties; Coefficients; Economic Theory/Quantitative Economics/Mathematical Methods; Estimating techniques; Estimators; Generalized linear models; International; Iterative methods; Mathematical analysis; Mathematical models; Mathematics and Statistics; Original Paper; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Regression; Statistics; Studies; Survival analysis; Europe; Primary 62G05; Weighted quasi-likelihood; Nonparametric estimation; Secondary 62G08; Generalized varying-coefficient models; Local polynomial regression
• ISSN: 0943-4062; 1613-9658
• DOI: 10.1007/s00180-015-0579-5

This paper focuses on the estimation of the coefficient functions, which is of primary interest, in generalized varying-coefficient models with non-exponential family error. The local weighted quasi-likelihood method which results from local polynomial regression techniques is presented. The nonparametric estimator based on iterative weighted quasi-likelihood method is obtained to estimate coefficient functions. The asymptotic efficiency of the proposed estimator is given. Furthermore, some simulations are carried out to evaluate the finite sample performance of the proposed method, which show that it possesses some advantages to the previous methods. Finally, a real data example is used to illustrate the proposed methodology.