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Finite mixture modeling of censored regression models

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  • Maria Karlsson

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  • 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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    1. repec:eee:jmvana:v:159:y:2017:i:c:p:151-167 is not listed on IDEAS
    2. repec:spr:stpapr:v:58:y:2017:i:2:d:10.1007_s00362-015-0709-8 is not listed on IDEAS

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