Comparing two nonparametric regression curves in the presence of long memory in covariates and errors

Hira L. Koul and Fang Li ()
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Hira L. Koul: Michigan State University
Fang Li: Indiana University Purdue University Indianapolis (IUPUI)

Metrika: International Journal for Theoretical and Applied Statistics, 2020, vol. 83, issue 4, No 5, 499-517

Abstract: Abstract This paper discusses the problem of testing the equality of two nonparametric regression functions against two-sided alternatives in the presence of long memory in the common covariate and errors. The proposed test is based on a marked empirical process of the differences between the response variables. We discuss asymptotic null distribution of this process and consistency of the test for a class of general alternatives. We also conduct a Monte Carlo simulation study to evaluate the finite sample level and power behavior of the test at some alternatives.

Keywords: Marked empirical process; Long memory process; Nonparametric regression; Random design; Primary 62M10; Secondary 62F03 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00184-019-00735-4

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