期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 42, 期 9, 页码 2040-2055出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2012.689064
关键词
Boundary effects; Coverage probability; k-factor; LOESS; Nonlinear regression; Nonparametric regression; Primary 62G15; Secondary 62G08
In this article, we discuss the utility of tolerance intervals for various regression models. We begin with a discussion of tolerance intervals for linear and nonlinear regression models. We then introduce a novel method for constructing nonparametric regression tolerance intervals by extending the well-established procedure for univariate data. Simulation results and application to real datasets are presented to help visualize regression tolerance intervals and to demonstrate that the methods we discuss have coverage probabilities very close to the specified nominal confidence level.
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