Empirical likelihood inference for generalized additive partially linear models

Rong Liu () and Yichuan Zhao ()
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Rong Liu: The University of Toledo
Yichuan Zhao: Georgia State University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2021, vol. 30, issue 3, No 2, 569-585

Abstract: Abstract Generalized additive partially linear models enjoy the simplicity of GLMs and the flexibility of GAMs because they combine both parametric and nonparametric components. Based on spline-backfitted kernel estimator, we propose empirical likelihood (EL)-based pointwise confidence intervals and simultaneous confidence bands (SCBs) for the nonparametric component functions to make statistical inference. Simulation study strongly supports the asymptotic theory and shows that EL-based SCBs are much easier for implementation and have better performance than Wald-type SCBs. We apply the proposed method to a university retention study and provide SCBs for the effect of the students information.

Keywords: Empirical likelihood; Kernel; Spline; Confidence bands; GAM; Primary 62G08; 62G15; Secondary 62G32 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11749-020-00731-1

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