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 |
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Pages (from-to) | 1532-1542 |
Number of pages | 11 |
Journal | Statistical Methods in Medical Research |
Volume | 26 |
Issue number | 3 |
DOIs | |
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