Nonparametric comparison of regression curves by local linear fitting

Tue Gørgens

doi:10.1016/S0167-7152(02)00283-3

Nonparametric comparison of regression curves by local linear fitting

Tue Gørgens^*

^*Corresponding author for this work

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

7 Citations (Scopus)

Abstract

This paper proposes a new nonparametric test for the hypothesis that the regression functions in two or more populations are the same. The test is based on local linear estimates using data-driven bandwidth selectors. The test is applicable to data with random regressors and heteroskedastic responses. Simulations indicate the test has good power.

Original language	English
Pages (from-to)	81-89
Number of pages	9
Journal	Statistics and Probability Letters
Volume	60
Issue number	1
DOIs	https://doi.org/10.1016/S0167-7152(02)00283-3
Publication status	Published - 1 Nov 2002

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10.1016/S0167-7152(02)00283-3

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title = "Nonparametric comparison of regression curves by local linear fitting",

abstract = "This paper proposes a new nonparametric test for the hypothesis that the regression functions in two or more populations are the same. The test is based on local linear estimates using data-driven bandwidth selectors. The test is applicable to data with random regressors and heteroskedastic responses. Simulations indicate the test has good power.",

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author = "Tue G{\o}rgens",

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AB - This paper proposes a new nonparametric test for the hypothesis that the regression functions in two or more populations are the same. The test is based on local linear estimates using data-driven bandwidth selectors. The test is applicable to data with random regressors and heteroskedastic responses. Simulations indicate the test has good power.

KW - Bias correction

KW - Local polynomial fitting

KW - Nonparametric testing

UR - https://www.scopus.com/pages/publications/0036839349

U2 - 10.1016/S0167-7152(02)00283-3

DO - 10.1016/S0167-7152(02)00283-3

M3 - Article

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VL - 60

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EP - 89

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 1

ER -

Nonparametric comparison of regression curves by local linear fitting

Abstract

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