Testing homogeneity in clustered (longitudinal) count data regression model with over-dispersion

2012 ◽  
Vol 142 (6) ◽  
pp. 1608-1618
Author(s):  
Sudhir Paul ◽  
Kazi Azad
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Data Regression ◽  
Longitudinal Count Data ◽  
Testing Homogeneity ◽  
Over Dispersion

PARAMETER ESTIMATION ON HURDLE POISSON REGRESSION MODEL WITH CENSORED DATA

2012 ◽  
Vol 57 (1) ◽  
Author(s):  
SEYED EHSAN SAFFAR ◽  
ROBIAH ADNAN ◽  
WILLIAM GREENE
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Goodness Of Fit ◽  
Poisson Model ◽  
Likelihood Method ◽  
Poisson Regression Model ◽  
Response Variable ◽  
The Mean ◽  
Over Dispersion

A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the same as before. In fact, the variance value of the dependent variable will be much more than the mean value of the dependent variable and this is called over–dispersion. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, it is suggested to use a hurdle Poisson regression model to overcome over–dispersion problem. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle Poisson regression model is introduced on count data with many zeros. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness–of–fit for the regression model is examined. We study the effects of right censoring on estimated parameters and their standard errors via an example.

A Flexible Count Data Regression Model for Risk Analysis

Risk Analysis ◽  
2008 ◽  
Vol 28 (1) ◽  
pp. 213-223 ◽  
Author(s):  
Seth D. Guikema ◽  
Jeremy P. Goffelt
Keyword(s):  
Risk Analysis ◽  
Regression Model ◽  
Count Data ◽  
Data Regression

scholarly journals The Effect of Sample Size on the Efficiency of Count Data Models: Application to Marriage Data

2017 ◽  
Vol 9 (3) ◽  
pp. 6
Author(s):  
Volition Tlhalitshi Montshiwa ◽  
Ntebogang Dinah Moroke
Keyword(s):  
Regression Model ◽  
Sample Size ◽  
Count Data ◽  
Negative Binomial ◽  
Information Criterion ◽  
Data Models ◽  
Hurdle Model ◽  
Negative Binomial Regression Model ◽  
Count Data Models ◽  
Over Dispersion

Abstract: Sample size requirements are common in many multivariate analysis techniques as one of the measures taken to ensure the robustness of such techniques, such requirements have not been of interest in the area of count data models. As such, this study investigated the effect of sample size on the efficiency of six commonly used count data models namely: Poisson regression model (PRM), Negative binomial regression model (NBRM), Zero-inflated Poisson (ZIP), Zero-inflated negative binomial (ZINB), Poisson Hurdle model (PHM) and Negative binomial hurdle model (NBHM). The data used in this study were sourced from Data First and were collected by Statistics South Africa through the Marriage and Divorce database. PRM, NBRM, ZIP, ZINB, PHM and NBHM were applied to ten randomly selected samples ranging from 4392 to 43916 and differing by 10% in size. The six models were compared using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Vuong’s test for over-dispersion, McFadden RSQ, Mean Square Error (MSE) and Mean Absolute Deviation (MAD).The results revealed that generally, the Negative Binomial-based models outperformed Poisson-based models. However, the results did not reveal the effect of sample size variations on the efficiency of the models since there was no consistency in the change in AIC, BIC, Vuong’s test for over-dispersion, McFadden RSQ, MSE and MAD as the sample size increased.

scholarly journals The Effect of Sample Size on the Efficiency of Count Data Models: Application to Marriage Data

2017 ◽  
Vol 9 (3(J)) ◽  
pp. 6-18
Author(s):  
Volition Tlhalitshi Montshiwa ◽  
Ntebogang Dinah Moroke
Keyword(s):  
Regression Model ◽  
Sample Size ◽  
Count Data ◽  
Negative Binomial ◽  
Information Criterion ◽  
Data Models ◽  
Hurdle Model ◽  
Negative Binomial Regression Model ◽  
Count Data Models ◽  
Over Dispersion

Abstract: Sample size requirements are common in many multivariate analysis techniques as one of the measures taken to ensure the robustness of such techniques, such requirements have not been of interest in the area of count data models. As such, this study investigated the effect of sample size on the efficiency of six commonly used count data models namely: Poisson regression model (PRM), Negative binomial regression model (NBRM), Zero-inflated Poisson (ZIP), Zero-inflated negative binomial (ZINB), Poisson Hurdle model (PHM) and Negative binomial hurdle model (NBHM). The data used in this study were sourced from Data First and were collected by Statistics South Africa through the Marriage and Divorce database. PRM, NBRM, ZIP, ZINB, PHM and NBHM were applied to ten randomly selected samples ranging from 4392 to 43916 and differing by 10% in size. The six models were compared using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Vuong’s test for over-dispersion, McFadden RSQ, Mean Square Error (MSE) and Mean Absolute Deviation (MAD).The results revealed that generally, the Negative Binomial-based models outperformed Poisson-based models. However, the results did not reveal the effect of sample size variations on the efficiency of the models since there was no consistency in the change in AIC, BIC, Vuong’s test for over-dispersion, McFadden RSQ, MSE and MAD as the sample size increased.

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