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Finite mixture modeling of censored regression models

Author

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  • Maria Karlsson
  • Thomas Laitila
Abstract
A finite mixture of Tobit models is suggested for estimation of regression models with a censored response variable. A mixture of models is not primarily adapted due to a true component structure in the population; the flexibility of the mixture is suggested as a way of avoiding non-robust parametrically specified models. The new estimator has several interesting features. One is its potential to yield valid estimates in cases with a high degree of censoring. The estimator is in a Monte Carlo simulation compared with earlier suggestions of estimators based on semi-parametric censored regression models. Simulation results are partly in favor of the proposed estimator and indicate potentials for further improvements. Copyright The Author(s) 2014

Suggested Citation

  • Maria Karlsson & Thomas Laitila, 2014. "Finite mixture modeling of censored regression models," Statistical Papers, Springer, vol. 55(3), pages 627-642, August.
    • Handle: RePEc:spr:stpapr:v:55:y:2014:i:3:p:627-642
    DOI: 10.1007/s00362-013-0509-y
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    References listed on IDEAS

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    3. Mirfarah, Elham & Naderi, Mehrdad & Chen, Ding-Geng, 2021. "Mixture of linear experts model for censored data: A novel approach with scale-mixture of normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    4. Wan-Lun Wang & Luis M. Castro & Wan-Chen Hsieh & Tsung-I Lin, 2021. "Mixtures of factor analyzers with covariates for modeling multiply censored dependent variables," Statistical Papers, Springer, vol. 62(5), pages 2119-2145, October.
    5. Lachos, Víctor H. & Moreno, Edgar J. López & Chen, Kun & Cabral, Celso Rômulo Barbosa, 2017. "Finite mixture modeling of censored data using the multivariate Student-t distribution," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 151-167.
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    7. Víctor H. Lachos & Celso R. B. Cabral & Marcos O. Prates & Dipak K. Dey, 2019. "Flexible regression modeling for censored data based on mixtures of student-t distributions," Computational Statistics, Springer, vol. 34(1), pages 123-152, March.
    8. Francisco H. C. Alencar & Larissa A Matos & Víctor H. Lachos, 2022. "Finite Mixture of Censored Linear Mixed Models for Irregularly Observed Longitudinal Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 463-486, November.
    9. Camila Borelli Zeller & Celso Rômulo Barbosa Cabral & Víctor Hugo Lachos & Luis Benites, 2019. "Finite mixture of regression models for censored data based on scale mixtures of normal distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 89-116, March.
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    11. Angelo Mazza & Antonio Punzo, 2020. "Mixtures of multivariate contaminated normal regression models," Statistical Papers, Springer, vol. 61(2), pages 787-822, April.
    12. Francisco H. C. Alencar & Christian E. Galarza & Larissa A. Matos & Victor H. Lachos, 2022. "Finite mixture modeling of censored and missing data using the multivariate skew-normal distribution," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 521-557, September.
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