Empirical likelihood for single-index varying-coefficient models

By Liugen Xue, Qihua Wang. 2012

55 citations recorded by the source corpus.

In this paper, we develop statistical inference techniques for the unknown coefficient functions and single-index parameters in single-index varying-coefficient models. We first estimate the nonparametric component via the local linear fitting, then construct an estimated empirical likelihood ratio function and hence obtain a maximum empirical likelihood estimator for the parametric component. Our estimator for parametric component is asymptotically efficient, and the estimator of nonparametric component has an optimal convergence rate. Our results provide ways to construct the confidence region for the involved unknown parameter. We also develop an adjusted empirical likelihood ratio for constructing the confidence regions of parameters of interest. A simulation study is conducted to evaluate the finite sample behaviors of the proposed methods.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5d362a20679b68bd24d32b0aa5c77d586c738c53

Citations in the corpus

• Varying Index Coefficient Models (2015, unknown): “We begin by noting that in the VICM (2), the loading coefficient vectors β l vary with X-covariates as opposed to a common loading coefficient vector assumed in the single-index coefficient model (SICM) proposed by Xia and Li (1999), Fan, Yao, and Cai (2003), and Xue and Wang (2012).”