期刊
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
卷 51, 期 2, 页码 427-447出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610926.2020.1749855
关键词
Kernel smoothing; Beta kernel regression; boundary effects; consistency and asymptotic normality
In this paper, an adaptive nonparametric regression estimation procedure with a finite interval support for the covariate is proposed. The kernel estimator based on the Beta density function is investigated for its large sample properties, including asymptotic normality and uniform convergence. Guidelines for bandwidth selection using data-driven approach are suggested. The finite sample performance of the proposed estimator is evaluated through simulation study and real data application.
In this paper, we propose an adaptive nonparametric regression estimation procedure when the covariate is supported over a finite interval. Unlike the classical symmetric kernel regression, the proposed kernel estimator is based on the Beta density function. Its large sample properties, including the asymptotic normality and the uniform convergence, are thoroughly investigated. Meanwhile, general data-driven guidelines for the bandwidth selection are suggested. The finite sample performance of the proposed estimator is evaluated via a simulation study and a real data application.
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