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Hiroaki Masuhara
 
''Identifying finite mixture models in the presence of moment-generating function: application in medical care using a zero-inflated binomial model''
( 2019, Vol. 39 No.2 )
 
 
This study presents a simple method to identify the parameters in finite mixture models when a moment-generating function (MGF) is present. We obtain the model conditions using a zero-inflated binomial model, a simple form of the finite mixture binary model, and analyze the results using the Monte Carlo simulation. Using the zero-inflated and standard binomial models, we compare the marginal effects of health care usage.
 
 
Keywords: Finite mixtures, identifiability, zero-inflated
JEL: C4 - Econometric and Statistical Methods: Special Topics
C1 - Econometric and Statistical Methods: General
 
Manuscript Received : Apr 03 2018 Manuscript Accepted : Jun 15 2019

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