The empirical likelihood goodness-of-fit test for regression model

Lixing ZHU; Yong Song Qin; Wang Li Xu

doi:10.1007/s11425-007-0044-1

The empirical likelihood goodness-of-fit test for regression model

Lixing ZHU^*, Yong Song Qin, Wang Li Xu

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to journal › Journal article › peer-review

9 Citations (Scopus)

Abstract

Goodness-of-fit test for regression modes has received much attention in literature. In this paper, empirical likelihood (EL) goodness-of-fit tests for regression models including classical parametric and autoregressive (AR) time series models are proposed. Unlike the existing locally smoothing and globally smoothing methodologies, the new method has the advantage that the tests are self-scale invariant and that the asymptotic null distribution is chi-squared. Simulations are carried out to illustrate the methodology.

Original language	English
Pages (from-to)	829-840
Number of pages	12
Journal	Science China Mathematics
Volume	50
Issue number	6
DOIs	https://doi.org/10.1007/s11425-007-0044-1
Publication status	Published - Jun 2007

Scopus Subject Areas

General Mathematics

User-Defined Keywords

AR time series models
Asymptotic normality
Empirical likelihood
Goodness-of-fit
Regression model

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10.1007/s11425-007-0044-1

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title = "The empirical likelihood goodness-of-fit test for regression model",

abstract = "Goodness-of-fit test for regression modes has received much attention in literature. In this paper, empirical likelihood (EL) goodness-of-fit tests for regression models including classical parametric and autoregressive (AR) time series models are proposed. Unlike the existing locally smoothing and globally smoothing methodologies, the new method has the advantage that the tests are self-scale invariant and that the asymptotic null distribution is chi-squared. Simulations are carried out to illustrate the methodology.",

keywords = "AR time series models, Asymptotic normality, Empirical likelihood, Goodness-of-fit, Regression model",

author = "Lixing ZHU and Qin, {Yong Song} and Xu, {Wang Li}",

note = "Funding Information: Received December 12, 2005; accepted November 2, 2006 DOI: 10.1007/s11425-007-0044-1 † Corresponding author This work was supported by the Research Grants Council Science Foundation of China (Grant No. 10661003)",

year = "2007",

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doi = "10.1007/s11425-007-0044-1",

language = "English",

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AU - ZHU, Lixing

AU - Qin, Yong Song

AU - Xu, Wang Li

N1 - Funding Information: Received December 12, 2005; accepted November 2, 2006 DOI: 10.1007/s11425-007-0044-1 † Corresponding author This work was supported by the Research Grants Council Science Foundation of China (Grant No. 10661003)

PY - 2007/6

Y1 - 2007/6

N2 - Goodness-of-fit test for regression modes has received much attention in literature. In this paper, empirical likelihood (EL) goodness-of-fit tests for regression models including classical parametric and autoregressive (AR) time series models are proposed. Unlike the existing locally smoothing and globally smoothing methodologies, the new method has the advantage that the tests are self-scale invariant and that the asymptotic null distribution is chi-squared. Simulations are carried out to illustrate the methodology.

AB - Goodness-of-fit test for regression modes has received much attention in literature. In this paper, empirical likelihood (EL) goodness-of-fit tests for regression models including classical parametric and autoregressive (AR) time series models are proposed. Unlike the existing locally smoothing and globally smoothing methodologies, the new method has the advantage that the tests are self-scale invariant and that the asymptotic null distribution is chi-squared. Simulations are carried out to illustrate the methodology.

KW - AR time series models

KW - Asymptotic normality

KW - Empirical likelihood

KW - Goodness-of-fit

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EP - 840

JO - Science China Mathematics

JF - Science China Mathematics

IS - 6

ER -

The empirical likelihood goodness-of-fit test for regression model

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