Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros

2014 ◽  
Vol 33 (25) ◽  
pp. 4402-4419 ◽  
Author(s):  
Wondwosen Kassahun ◽  
Thomas Neyens ◽  
Geert Molenberghs ◽  
Christel Faes ◽  
Geert Verbeke
Keyword(s):  
Count Data ◽  
Excess Zeros

scholarly journals Transition models for count data: a flexible alternative to fixed distribution models

2021 ◽  
Author(s):  
Moritz Berger ◽  
Gerhard Tutz
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Real Data ◽  
Distribution Models ◽  
Explanatory Variables ◽  
Excess Zeros ◽  
Proposed Model ◽  
Transition Models ◽  
Fixed Distribution

AbstractA flexible semiparametric class of models is introduced that offers an alternative to classical regression models for count data as the Poisson and Negative Binomial model, as well as to more general models accounting for excess zeros that are also based on fixed distributional assumptions. The model allows that the data itself determine the distribution of the response variable, but, in its basic form, uses a parametric term that specifies the effect of explanatory variables. In addition, an extended version is considered, in which the effects of covariates are specified nonparametrically. The proposed model and traditional models are compared in simulations and by utilizing several real data applications from the area of health and social science.

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

scholarly journals A Study of ZIP and ZINB Regression Modeling for Count Data with Excess Zeros

2021 ◽  
Vol 1863 (1) ◽  
pp. 012022
Author(s):  
R N Amalia ◽  
K Sadik ◽  
K A Notodiputro
Keyword(s):  
Count Data ◽  
Regression Modeling ◽  
Excess Zeros

scholarly journals Response to comments on “Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros”

2018 ◽  
Vol 37 (11) ◽  
pp. 1942-1946
Author(s):  
Geert Molenberghs ◽  
Alvaro Florez Poveda ◽  
Wondwosen Kassahun ◽  
Thomas Neyens ◽  
Christel Faes ◽  
...  
Keyword(s):  
Count Data ◽  
Excess Zeros

scholarly journals Network analysis for count data with excess zeros

BMC Genetics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Hosik Choi ◽  
Jungsoo Gim ◽  
Sungho Won ◽  
You Jin Kim ◽  
Sunghoon Kwon ◽  
...  
Keyword(s):  
Network Analysis ◽  
Count Data ◽  
Excess Zeros

Semiparametric models for multilevel overdispersed count data with extra zeros

2016 ◽  
Vol 27 (4) ◽  
pp. 1187-1201 ◽  
Author(s):  
Marzieh Mahmoodi ◽  
Abbas Moghimbeigi ◽  
Kazem Mohammad ◽  
Javad Faradmal
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Semiparametric Models ◽  
Multilevel Regression ◽  
Monte Carlo Simulation Study ◽  
Excess Zeros ◽  
Generalized Poisson ◽  
Comprehensive Comparison

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.

scholarly journals Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches

2021 ◽  
Author(s):  
John Haslett ◽  
Andrew C. Parnell ◽  
John Hinde ◽  
Rafael Andrade Moral
Keyword(s):  
Count Data ◽  
Excess Zeros ◽  
New Perspective ◽  
Modelling Approaches

scholarly journals Correcting for Non-Sum to 1 Estimated Probabilities in Applications of Discrete Probability Models to Count Data

2017 ◽  
Vol 6 (5) ◽  
pp. 119
Author(s):  
Bayo H. Lawal
Keyword(s):  
Sample Size ◽  
Count Data ◽  
Negative Binomial ◽  
Frequency Count ◽  
Frequency Data ◽  
Probability Models ◽  
Common Procedure ◽  
Discrete Probability ◽  
Excess Zeros ◽  
Expected Values

In this paper, we examine some often ignored or assumed problems relating with fitting probability models to count data either exhibiting over, equi, or under dispersion. Of particular concern are last category truncated data, where most often, expected values in this last category are collapsed together so that the sum of the expected values sum to the sample size in the data. That is, so that $\displaystyle \sum_{i=0}^{k} \hat{m}_i=n$, the sample size. We shall for illustrative purposes in this paper, consider the following distributions: the negative binomial (NB), the Inverse trinomial (IT), the hyper-Poisson (HP), the Quasi-negative binomial (QNBD), the extended com-Poisson distribution (ECOMP) as well as the negative binomial-exponential distribution (NBGE).Though, we have restricted our discussion to these six distributions, other distributions may also be employed but the patterns are always the same, that is, the sum of the estimated probabilities does not equal 1.00 and consequently, the sum of the expected values is always less or equal (Poisson case only) the sample size in the observed data. We propose a common procedure to rectify this problem for both right truncated or non-truncated frequency count data exhibiting either excess zeros or regular frequency data.

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