Distribution-free estimation of zero-inflated models with unobserved heterogeneity

Rodica Gilles; Seik Kim

doi:10.1177/0962280215588940

Distribution-free estimation of zero-inflated models with unobserved heterogeneity

Rodica Gilles, Seik Kim

Department of Economics

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

This paper presents a quasi-conditional likelihood method for the consistent estimation of both continuous and count data models with excess zeros and unobserved individual heterogeneity when the true data generating process is unknown. Monte Carlo simulation studies show that our zero-inflated quasi-conditional maximum likelihood (ZI-QCML) estimator outperforms other methods and is robust to distributional misspecifications. We apply the ZI-QCML estimator to analyze the frequency of doctor visits.

Original language	English
Pages (from-to)	1532-1542
Number of pages	11
Journal	Statistical Methods in Medical Research
Volume	26
Issue number	3
DOIs	https://doi.org/10.1177/0962280215588940
Publication status	Published - 2017 Jun 1

Keywords

Excess zeros
nonnegative data
quasi-likelihood estimation
robust estimation
zero inflation

ASJC Scopus subject areas

Epidemiology
Statistics and Probability
Health Information Management

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AB - This paper presents a quasi-conditional likelihood method for the consistent estimation of both continuous and count data models with excess zeros and unobserved individual heterogeneity when the true data generating process is unknown. Monte Carlo simulation studies show that our zero-inflated quasi-conditional maximum likelihood (ZI-QCML) estimator outperforms other methods and is robust to distributional misspecifications. We apply the ZI-QCML estimator to analyze the frequency of doctor visits.

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KW - nonnegative data

KW - quasi-likelihood estimation

KW - robust estimation

KW - zero inflation

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Distribution-free estimation of zero-inflated models with unobserved heterogeneity

Abstract

Keywords

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