Influence diagnostics for linear models with first-order autoregressive elliptical errors

Paula, Gilberto A.; Medeiros, Márcio José de; Vilca, Filidor

doi:10.1016/j.spl.2008.08.017

Cited by 33 publications

(20 citation statements)

References 12 publications

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“…For the linear models, such as Eubank and Thomas [5], Galea et al [10], Cysneiros et al [3], Paula et al [21] and Lin et al [19]. For the nonlinear models, there are Wei et al [24], Dette [4], Lin and Wei [17,18], Galea et al [9], Cao et al [2], Lin and Qu [16] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…Cysneiros et al (2007) derived diagnostics for symmetrical heteroscedastic linear models. Paula et al (2009) studied some influence diagnostics under the elliptical and AR(1) error structure. Cao et al (2010) considered heteroscedasticity and autocorrelation diagnostics for the symmetrical and AR(1) nonlinear model.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the work of Paula et al (2009) and Cao et al (2010), this article will discuss some diagnostics, including score test of autocorrelation and local influences, for the symmetrical and AR(1) linear model. Note that, the model errors we work with is AR(1) and conditional symmetric ones, which has a essential difference with the elliptical case discussed by Paula et al (2009).…”

Section: Introductionmentioning

confidence: 99%

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Diagnostics for a Linear Model with First-Order Autoregressive Symmetrical Errors

Zhu

Cao

2013

Communications in Statistics - Theory and Methods

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In this article, we focus on some diagnostics for linear regression model with firstorder autoregressive and symmetrical errors. The symmetrical class includes both light-and heavy-tailed univariate symmetrical distributions, which offers a more flexible framework for modeling. Maximum likelihood estimates are computed via the Fisher-score method. Score statistic and its adjustment are proposed for testing autocorrelation of the random errors. Local influence diagnostics are also derived for the model under some usual perturbation schemes. The performances of the test statistics are investigated through Monte Carlo simulations. Finally, a real data set is used to illustrate our diagnostic methods.

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“…This method has had a great impact in the literature of the topic in the last decades. Several authors have extended the technique of local influence to more general regression models; see, e.g., Escobar and Meeker (1992), Paula (1993), Lawrence (1998), Galea et al (1997Galea et al ( , 2000Galea et al ( , 2004, Ortega et al (2003), Díaz-García et al (2003), Leiva et al (2007), Barros et al (2008), and Paula et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Influence diagnostics in the tobit censored response model

Barros

Galea

Rodríguez³

et al. 2010

Stat Methods Appl

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Influence diagnostics for linear models with first-order autoregressive elliptical errors

Cited by 33 publications

References 12 publications

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

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

Diagnostics for a Linear Model with First-Order Autoregressive Symmetrical Errors

Influence diagnostics in the tobit censored response model

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