Empirical likelihood inferences for varying coefficient partially nonlinear models. Issue 3 (17th February 2017)

• Record Type: Journal Article
• Authors: Zhou, Xiaoshuang; Zhao, Peixin; Wang, Xiuli
• Is Part Of: Journal of applied statistics. Volume 44:Issue 3(2017)
• Page Start: 474
• Page End: 492
• Publication Date: 2017-02-17
• Subjects: Empirical likelihood -- varying coefficient partially nonlinear model -- profile nonlinearleast-squares estimation -- confidence region -- parameter vector; 62G05 -- 62G20; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1177496
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: In this article, empirical likelihood inferences for the varying coefficient partially nonlinear models are investigated. An empirical log-likelihood ratio function for the unknown parameter vector in the nonlinear function part and a residual-adjusted empirical log-likelihood ratio function for the nonparametric component are proposed. The corresponding Wilks phenomena are proved and the confidence regions for parametric component and nonparametric component are constructed. Simulation studies indicate that, in terms of coverage probabilities and average areas of the confidence regions, the empirical likelihood method performs better than the normal approximation-based method. Furthermore, a real data set application is also provided to illustrate the proposed empirical likelihood estimation technique.