Heteroscedasticity and/or autocorrelation diagnostics in nonlinear models with AR(1) and symmetrical errors

Cao, Chun Zheng; Lin, Jin Guan; Zhu, Lixing

doi:10.1007/s00362-008-0171-y

Cited by 18 publications

(26 citation statements)

References 25 publications

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“…Lin et al [14] considered heteroscedasticity diagnostics for t linear regression models. Cao et al [1] extended their results to nonlinear regression models with symmetrical and AR(1) errors.…”

Section: Introductionmentioning

confidence: 94%

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Heteroscedasticity and/or autocorrelation checks in longitudinal nonlinear models with elliptical and AR(1) errors

Cao

Lin

2011

Acta Math. Appl. Sin. Engl. Ser.

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The aim of this paper is to study the tests for variance heterogeneity and/or autocorrelation in nonlinear regression models with elliptical and AR(1) errors. The elliptical class includes several symmetric multivariate distributions such as normal, Student-t, power exponential, among others. Several diagnostic tests using score statistics and their adjustment are constructed. The asymptotic properties, including asymptotic chi-square and approximate powers under local alternatives of the score statistics, are studied. The properties of test statistics are investigated through Monte Carlo simulations. A data set previously analyzed under normal errors is reanalyzed under elliptical models to illustrate our test methods.

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Section: Introductionmentioning

confidence: 94%

“…Chen [2] pointed out that heteroscedasticity is not very sensitive to the functional form of relationship between variances and z T ij γ γ γ. Now we consider the tests of heteroscedasticity and/or autocorrelation for random errors in model (1). We denote…”

Section: The Models and Testsmentioning

confidence: 99%

Heteroscedasticity and/or autocorrelation checks in longitudinal nonlinear models with elliptical and AR(1) errors

Cao

Lin

2011

Acta Math. Appl. Sin. Engl. Ser.

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“…Therefore, detecting the variance heterogeneity in regression models is an importance and necessary step in statistical inference. To our best knowledge, there does not exist a published heteroscedasticity test for model (2). The goal of this paper is to develop such a test.…”

Section: Introductionmentioning

confidence: 96%

“…The importance of being able to detect heteroscedasticity is widely recognized because, if the assumption of homoscedasticity is not met, efficient inference of the model (2) requires that the heteroscedasticity is taken into account. Therefore, detecting the variance heterogeneity in regression models is an importance and necessary step in statistical inference.…”

Section: Introductionmentioning

confidence: 99%

“…Of interest is to examine the association between the covariables and the median value of owner-occupied homes, and check whether there exists heteroscedastic error in the considered model or not. It is natural to allow the association to change over percentage of lower income status of the population covarible; such a problem can be tackled by using Equation (2) as follows. In the model, u is the percentage of lower income status of the population covariate, and X denotes the other covariables.…”

Section: Introductionmentioning

confidence: 99%

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Heteroscedasticity diagnostics in varying-coefficient partially linear regression models and applications in analyzing Boston housing data

Lin

Zhao

Wang

2015

Journal of Applied Statistics

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It is important to detect the variance heterogeneity in regression model because efficient inference requires that heteroscedasticity is taken into consideration if it really exists. For the varying-coefficient partially linear regression models, however, the problem of detecting heteroscedasticity has received very little attention. In this paper, we present two classes of tests of heteroscedasticity for varying-coefficient partially linear regression models. The first test statistic is constructed based on the residuals, in which the error term is from a normal distribution. The second one is motivated by the idea that testing heteroscedasticity is equivalent to testing pseudo-residuals for a constant mean. Asymptotic normality is established with different rates corresponding to the null hypothesis of homoscedasticity and the alternative. Some Monte Carlo simulations are conducted to investigate the finite sample performance of the proposed tests. The test methodologies are illustrated with a real data set example.

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Additive models with autoregressive symmetric errors based on penalized regression splines

Oliveira

Paula

2021

Comput Stat

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Heteroscedasticity and/or autocorrelation diagnostics in nonlinear models with AR(1) and symmetrical errors

Abstract: Symmetrical distributions, Nonlinear model, AR(1) errors, Heteroscedasticity, Score test, Asymptotic properties, Approximate local powers,

Cited by 18 publications

References 25 publications

Heteroscedasticity and/or autocorrelation checks in longitudinal nonlinear models with elliptical and AR(1) errors

Heteroscedasticity and/or autocorrelation checks in longitudinal nonlinear models with elliptical and AR(1) errors

Heteroscedasticity diagnostics in varying-coefficient partially linear regression models and applications in analyzing Boston housing data

Additive models with autoregressive symmetric errors based on penalized regression splines

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