Empirical likelihood based inference for generalized additive partial linear models

Zhuoxi Yu, Kai Yang and Milan Parmar

Applied Mathematics and Computation, 2018, vol. 339, issue C, 105-112

Abstract: Empirical-likelihood based inference for the parameters in generalized additive partial linear models (GAPLM) is investigated. With the use of the polynomial spline smoothing for estimation of nonparametric functions, an estimated empirical likelihood ratio statistic based on the quasi-likelihood equation is proposed. We show that the resulting statistic is asymptotically standard chi-squared distributed and the confidence regions for the parametric components are constructed. Some simulations are conducted to illustrate the proposed methods.

Keywords: Generalized Additive partial linear models; Empirical likelihood; Quasi-likelihood equation; χ2 distribution; Confidence region (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:339:y:2018:i:c:p:105-112

DOI: 10.1016/j.amc.2018.06.050

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