Semiparametric models for multilevel overdispersed count data with extra zeros.

Abstract:

:This study proposes semiparametric models for analysis of hierarchical count data containing excess zeros and overdispersion simultaneously. The methods discussed in this paper handle nonlinear covariate effects through flexible semiparametric multilevel regression techniques. This is performed by providing a comprehensive comparison of semiparametric multilevel zero-inflated negative binomial and semiparametric multilevel zero-inflated generalized Poisson models under the real and simulated data. An EM algorithm based on Newton-Raphson equations for maximum penalized likelihood estimation approach is developed. The performance of the proposed models is assessed by using a Monte Carlo simulation study. We also illustrated the methods by the analysis of decayed, missing, and filled teeth of children aged 5-14 years old.

journal_name

Stat Methods Med Res

authors

Mahmoodi M,Moghimbeigi A,Mohammad K,Faradmal J

doi

10.1177/0962280216657376

subject

Has Abstract

pub_date

2018-04-01 00:00:00

pages

1187-1201

issue

4

eissn

0962-2802

issn

1477-0334

pii

0962280216657376

journal_volume

27

pub_type

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