2015The Stata Journal Promoting communications on statistics and StataOpen access

Regression Models for Count Data from Truncated Distributions

James W. Hardin, Joseph M. Hilbe

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Abstract

We present new commands for analyzing count-data regression models for truncated distributions. The trncregress command allows specification of a regression model for the mean of the truncated distribution through options. In addition to support for truncated Poisson and negative binomial, trncregress fits models based on truncated versions of distributions including generalized Poisson, Poisson-inverse Gaussian, three-parameter negative binomial power, three-parameter Waring negative binomial, and three-parameter Famoye negative binomial.

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We present new commands for analyzing count-data regression models for truncated distributions. The trncregress command allows specification of a regression model for the mean of the truncated distribution through options. In addition to support for truncated Poisson and negative binomial, trncregress fits models based on truncated versions of distributions including generalized Poisson, Poisson-inverse Gaussian, three-parameter negative binomial power, three-parameter Waring negative binomial, and three-parameter Famoye negative binomial.

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Available abstract

We present new commands for analyzing count-data regression models for truncated distributions. The trncregress command allows specification of a regression model for the mean of the truncated distribution through options. In addition to support for truncated Poisson and negative binomial, trncregress fits models based on truncated versions of distributions including generalized Poisson, Poisson-inverse Gaussian, three-parameter negative binomial power, three-parameter Waring negative binomial, and three-parameter Famoye negative binomial.

Key concepts: Count data, Negative binomial distribution, Poisson distribution, Poisson regression, Quasi-likelihood, Mathematics, Statistics, Negative multinomial distribution

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