Miscellanea Nonparametric detection of correlated errors

Tab Yoon Kim; Donghoh Kim; Byeong U. Park; Douglas G. Simpson

doi:10.1093/biomet/91.2.491

Miscellanea Nonparametric detection of correlated errors

Tab Yoon Kim, Donghoh Kim, Byeong U. Park, Douglas G. Simpson

Research output: Contribution to journal › Article › peer-review

Abstract

In regression problems it is hard to detect correlated errors since the errors are not observed. In this paper, a nonparametric method is proposed for the detection of correlated errors when the design points are equally spaced. It turns out that the first-order sample autocovariance of the residuals from the kernel regression estimates provides essential information about correlated errors and its bootstrap is quite effective in implementing such information.

Original language	English (US)
Pages (from-to)	491-496
Number of pages	6
Journal	Biometrika
Volume	91
Issue number	2
DOIs	https://doi.org/10.1093/biomet/91.2.491
State	Published - 2004

Keywords

Bootstrap
Correlated errors
Kernel regression

ASJC Scopus subject areas

Statistics and Probability
General Mathematics
Agricultural and Biological Sciences (miscellaneous)
General Agricultural and Biological Sciences
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1093/biomet/91.2.491

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title = "Miscellanea Nonparametric detection of correlated errors",

abstract = "In regression problems it is hard to detect correlated errors since the errors are not observed. In this paper, a nonparametric method is proposed for the detection of correlated errors when the design points are equally spaced. It turns out that the first-order sample autocovariance of the residuals from the kernel regression estimates provides essential information about correlated errors and its bootstrap is quite effective in implementing such information.",

keywords = "Bootstrap, Correlated errors, Kernel regression",

author = "Kim, {Tab Yoon} and Donghoh Kim and Park, {Byeong U.} and Simpson, {Douglas G.}",

note = "Funding Information: The authors are grateful to the anonymous referee for valuable comments on the earlier version of the paper. The work of Tae Yoon Kim and Byeong U. Park was supported by grants from the Korea Science and Engineering Foundation and from the Korea Research Foundation, respectively. The work of Douglas G. Simpson was supported by the National Science Foundation.",

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doi = "10.1093/biomet/91.2.491",

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T1 - Miscellanea Nonparametric detection of correlated errors

AU - Kim, Tab Yoon

AU - Kim, Donghoh

AU - Park, Byeong U.

AU - Simpson, Douglas G.

N1 - Funding Information: The authors are grateful to the anonymous referee for valuable comments on the earlier version of the paper. The work of Tae Yoon Kim and Byeong U. Park was supported by grants from the Korea Science and Engineering Foundation and from the Korea Research Foundation, respectively. The work of Douglas G. Simpson was supported by the National Science Foundation.

PY - 2004

Y1 - 2004

N2 - In regression problems it is hard to detect correlated errors since the errors are not observed. In this paper, a nonparametric method is proposed for the detection of correlated errors when the design points are equally spaced. It turns out that the first-order sample autocovariance of the residuals from the kernel regression estimates provides essential information about correlated errors and its bootstrap is quite effective in implementing such information.

AB - In regression problems it is hard to detect correlated errors since the errors are not observed. In this paper, a nonparametric method is proposed for the detection of correlated errors when the design points are equally spaced. It turns out that the first-order sample autocovariance of the residuals from the kernel regression estimates provides essential information about correlated errors and its bootstrap is quite effective in implementing such information.

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KW - Correlated errors

KW - Kernel regression

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JF - Biometrika

IS - 2

ER -

Miscellanea Nonparametric detection of correlated errors

Abstract

Keywords

ASJC Scopus subject areas

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