AStA Advances in Statistical Analysis

, Volume 103, Issue 4, pp 503–526 | Cite as

A new approach to truncated regression for count data

  • Ana María Martínez-RodríguezEmail author
  • Antonio Conde-Sánchez
  • María José Olmo-Jiménez
Original Paper


Standard Poisson and negative binomial truncated regression models for count data include the regressors in the mean of the non-truncated distribution. In this paper, a new approach is proposed so that the explanatory variables determine directly the truncated mean. The main advantage is that the regression coefficients in the new models have a straightforward interpretation as the effect of a change in a covariate on the mean of the response variable. A simulation study has been carried out in order to analyze the performance of the proposed truncated regression models versus the standard ones showing that coefficient estimates are now more accurate in the sense that the standard errors are always lower. Also, the simulation study indicates that the estimates obtained with the standard models are biased. An application to real data illustrates the utility of the introduced truncated models in a hurdle model. Although in the example there are slight differences in the results between the two approaches, the proposed one provides a clear interpretation of the coefficient estimates.


Count data Hurdle model Negative binomial regression Poisson regression Truncated models 



We are grateful to the anonymous referees for their careful review and constructive comments that substantially improved the article.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Statistics and Operations ResearchUniversity of JaénJaénSpain

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