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Semiparametric additive models under symmetric distributions

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  • Germán Ibacache-Pulgar
  • Gilberto Paula
  • Francisco Cysneiros

Abstract

In this paper we discuss estimation and diagnostic procedures in semiparametric additive models with symmetric errors in order to permit distributions with heavier and lighter tails than the normal ones, such as Student-t, Pearson VII, power exponential, logistics I and II, and contaminated normal, among others. Such models belong to the general class of statistical models GAMLSS proposed by Rigby and Stasinopoulos (Appl. Stat. 54:507–554, 2005 ). A back-fitting algorithm to attain the maximum penalized likelihood estimates (MPLEs) by using natural cubic smoothing splines is presented. In particular, the score functions and Fisher information matrices for the parameters of interest are expressed in a similar notation of that used in parametric symmetric models. Sufficient conditions on the existence of the MPLEs are presented as well as some inferential results and discussions on degrees of freedom and smoothing parameter estimation. Diagnostic quantities such as leverage, standardized residual and normal curvatures of local influence under two perturbation schemes are derived. A real data set previously analyzed under normal linear models is reanalyzed under semiparametric additive models with symmetric errors. Copyright Sociedad de Estadística e Investigación Operativa 2013

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  • Germán Ibacache-Pulgar & Gilberto Paula & Francisco Cysneiros, 2013. "Semiparametric additive models under symmetric distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 103-121, March.
    • Handle: RePEc:spr:testjl:v:22:y:2013:i:1:p:103-121
    DOI: 10.1007/s11749-012-0309-z
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    References listed on IDEAS

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    5. Ibacache-Pulgar, Germán & Paula, Gilberto A., 2011. "Local influence for Student-t partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1462-1478, March.
    6. Cysneiros, Francisco Jose A. & Paula, Gilberto A., 2005. "Restricted methods in symmetrical linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 689-708, June.
    7. W.‐Y. Poon & Y. S. Poon, 1999. "Conformal normal curvature and assessment of local influence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 51-61.
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    9. Cysneiros, Francisco José A. & Paula, Gilberto A. & Galea, Manuel, 2007. "Heteroscedastic symmetrical linear models," Statistics & Probability Letters, Elsevier, vol. 77(11), pages 1084-1090, June.
    10. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
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    Cited by:

    1. Alejandra Tapia & Victor Leiva & Maria del Pilar Diaz & Viviana Giampaoli, 2019. "Influence diagnostics in mixed effects logistic regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 920-942, September.
    2. Nicole Jeldes & Germán Ibacache-Pulgar & Carolina Marchant & Javier Linkolk López-Gonzales, 2022. "Modeling Air Pollution Using Partially Varying Coefficient Models with Heavy Tails," Mathematics, MDPI, vol. 10(19), pages 1-24, October.
    3. Luis Vanegas & Gilberto Paula, 2015. "A semiparametric approach for joint modeling of median and skewness," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 110-135, March.
    4. Elton G. Aráujo & Julio C. S. Vasconcelos & Denize P. Santos & Edwin M. M. Ortega & Dalton Souza & João P. F. Zanetoni, 2023. "The Zero-Inflated Negative Binomial Semiparametric Regression Model: Application to Number of Failing Grades Data," Annals of Data Science, Springer, vol. 10(4), pages 991-1006, August.
    5. Kong, Dehan & Bondell, Howard D. & Wu, Yichao, 2015. "Domain selection for the varying coefficient model via local polynomial regression," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 236-250.
    6. Carlos Eduardo M. Relvas & Gilberto A. Paula, 2016. "Partially linear models with first-order autoregressive symmetric errors," Statistical Papers, Springer, vol. 57(3), pages 795-825, September.
    7. Rodrigo A. Oliveira & Gilberto A. Paula, 2021. "Additive models with autoregressive symmetric errors based on penalized regression splines," Computational Statistics, Springer, vol. 36(4), pages 2435-2466, December.
    8. Germán Ibacache-Pulgar & Cristian Villegas & Javier Linkolk López-Gonzales & Magaly Moraga, 2023. "Influence measures in nonparametric regression model with symmetric random errors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 1-25, March.

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