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Article Dans Une Revue Statistics Année : 2017

Inference in a generalized endpoint-inflated binomial regression model

Résumé

Generalized endpoint-inflated binomial regression was recently proposed to model count data with large frequencies of both zeros and right-endpoints. Maximum likelihood estimation (MLE) was developed for this model and simulations suggest that the resulting estimates behave well. However, large-sample properties of the MLE have not yet been rigorously established. Such results are however essential for ensuring reliable statistical inference and decision-making. This paper addresses this issue. Identifiability of the generalized endpoint-inflated binomial regression model is first proved. Then, consistency and asymptotic normality of the MLE are established. A simulation study is conducted to assess finite-sample behaviour of the estimator.
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Dates et versions

hal-01538348 , version 1 (13-06-2017)

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Citer

Jean-François Dupuy. Inference in a generalized endpoint-inflated binomial regression model. Statistics, 2017, 51 (4), pp.888-903. ⟨10.1080/02331888.2017.1316724⟩. ⟨hal-01538348⟩
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