Local influence for Student-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si59.gif" display="inline" overflow="scroll"><mml:mi>t</mml:mi></mml:math>partially linear models

Ibacache-Pulgar, Germán; Paula, Gilberto A.

doi:10.1016/j.csda.2010.10.009

Cited by 26 publications

(8 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, this paper generalizes the work by Ibacache-Pulgar and Paula [26] and Ibacache-Pulgar et al [27] from a Bayesian perspective by incorporating censoring information. We apply our method to a dataset from PSID panel data to illustrate how the procedure developed can be used to evaluate model assumptions, identify outliers and obtain robust parameter estimates.…”

Section: Discussionsupporting

confidence: 61%

Partially linear censored regression models using heavy-tailed distributions: A Bayesian approach

Castro

Lachos

Ferreira

et al. 2014

Statistical Methodology

View full text Add to dashboard Cite

Section: Discussionsupporting

confidence: 61%

Partially linear censored regression models using heavy-tailed distributions: A Bayesian approach

Castro

Lachos

Ferreira

et al. 2014

Statistical Methodology

View full text Add to dashboard Cite

“…In context of nonparametric and semiparametric regression models, Thomas (1991) constructed local influence diagnostics for the smoothing parameter and Zhu et al (2003) extended the works by Cook (1986) to provide local influence measures under different perturbation schemes in normal partially LMs. Ibacache-Pulgar and Paula (2011) extended the local influence methodology to Student-t partial LMs. Ibacache-Pulgar et al (2012, 2013 developed local influence for elliptical semiparamteric mixed model and semiparametric additive model under symmetric distributions respectively.…”

Section: Introductionmentioning

confidence: 99%

Local influence for elliptical partially varying-coefficient model

Ibacache-Pulgar

Reyes

2017

Statistical Modelling

Self Cite

View full text Add to dashboard Cite

In this article, we extend varying-coefficient models with normal errors to elliptical errors in order to permit distributions with heavier and lighter tails than the normal ones. This class of models includes all symmetric continuous distributions, such as Student-t, Pearson VII, power exponential and logistic, among others. Estimation is performed by maximum penalized likelihood method and by using smoothing splines. In order to study the sensitivity of the penalized estimates under some usual perturbation schemes in the model or data, the local influence curvatures are derived and some diagnostic graphics are proposed. A real dataset previously analysed by using varying-coefficient models with normal errors is reanalysed under varying-coefficient models with heavy-tailed errors.

show abstract

“…Os modelos parcialmente lineares contemplam tais situações em que o componente sistemático é formado por um termo paramétrico e outro termo não paramétrico. Por exemplo, em Kim et al [2002] há uma discussão sobre modelos parcialmente lineares com erros normais e Ibacache-Pulgar e Paula [2011] estendem os resultados de Kim et al [2002] para modelos parcialmente lineares com erros t de Student. Um livro clássico no assunto é Green e Silverman [1994].…”

Section: Preliminaresunclassified

Modelos parcialmente lineares com erros simétricos autoregressivos de primeira ordem

Relvas¹

View full text Add to dashboard Cite

Symmetric partially linear models with first-order autoregressive errors In this master dissertation, we present the symmetric partially linear models with AR(1) errors that generalize the normal partially linear models to contain autocorrelated errors AR(1) following a symmetric distribution instead of the normal distribution. Among the symmetric distributions, we can consider heavier tails than the normal ones, controlling the kurtosis and downweighting outlying observations in the estimation process. The parameter estimation is made through the penalized likelihood by using score functions and the expected Fisher information. We derive these functions in this work. The effective degrees of freedom and asymptotic results are also presented as well as the residual analysis, highlighting the normal curvature of local influence under different perturbation schemes. An application with real data is given for illustration.

show abstract

Local influence for Student-tpartially linear models

Cited by 26 publications

References 45 publications

Partially linear censored regression models using heavy-tailed distributions: A Bayesian approach

Partially linear censored regression models using heavy-tailed distributions: A Bayesian approach

Local influence for elliptical partially varying-coefficient model

Modelos parcialmente lineares com erros simétricos autoregressivos de primeira ordem

Contact Info

Product

Resources

About