scholarly journals Estimation and Hypothesis Testing for the Parameters of Multivariate Zero Inflated Generalized Poisson Regression Model

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1876
Author(s):  
Dewi Novita Sari ◽  
Purhadi Purhadi ◽  
Santi Puteri Rahayu ◽  
Irhamah Irhamah
Keyword(s):  
Em Algorithm ◽  
Regression Model ◽  
Hypothesis Testing ◽  
Poisson Regression ◽  
Type Ii ◽  
Maternal Deaths ◽  
Generalized Poisson ◽  
The Em Algorithm ◽  
Generalized Poisson Regression ◽  
Response Variables

We propose a multivariate regression model called Multivariate Zero Inflated Generalized Poisson Regression (MZIGPR) type II. This model further develops the Bivariate Zero Inflated Generalized Poisson Regression (BZIGPR) type II. This study aims to develop parameter estimation, test statistics, and hypothesis testing, both simultaneously and partially, for significant parameters of the MZIGPR model. The steps of the EM algorithm for obtaining the parameter estimator are also described in this article. We use Berndt–Hall–Hall–Hausman (BHHH) numerical iteration to optimize the EM algorithm. Simultaneous testing is carried out using the maximum likelihood ratio test (MLRT) and the Wald test to partially assess the hypothesis. The proposed MZIGPR model is then used to model the three response variables: the number of maternal childbirth deaths, the number of postpartum maternal deaths, and the number of stillbirths with four predictors. The units of observation are the sub-districts of the Pekalongan Residency, Indonesia. The indicate overdispersion in the data on the number of maternal childbirth deaths and stillbirths, and underdispersion in the data on the number of postpartum maternal deaths. The empirical studies show that the three response variables are significantly affected by all the predictor variables.

scholarly journals Modelling Generalized Poisson Regression in the Number of Dengue Hemorrhagic Fever (DHF) in East Nusa Tenggara

2020 ◽  
Vol 202 ◽  
pp. 12017
Author(s):  
Alan Prahutama ◽  
Dwi Ispriyanti ◽  
Budi Warsito
Keyword(s):  
Regression Model ◽  
Dengue Hemorrhagic Fever ◽  
Poisson Regression ◽  
Average Length ◽  
Dispersion Model ◽  
Hemorrhagic Fever ◽  
Poisson Regression Model ◽  
Generalized Poisson ◽  
Generalized Poisson Regression ◽  
Over Dispersion

Regression analysis is an analysis used to model the relationship between the dependent variable (Y) and the independent variable (X). If the dependent variable is a discrete random variable, it is developed using the Poisson regression model. Poisson regression models require non-over-dispersion model assumptions. To deal with over-dispersion, a Generalized Poisson regression model was developed. Generalized Poisson regression (GPR) model is an extension of the Poisson regression model. In this study a GPR model is applied to model the number of dengue hemorrhagic fever (DHF) sufferers in East Nusa Tenggara Province in 2018. The independent variables used include percentage of poor population (X1), population density (X2), percentage of proper sanitation (X3), percentage of decent homes (X4), number of doctors (X5), percentage of access to improved drinking water (X6), average length of schooling (X7), human development index (X8). In the resulting model, Poisson regression experiences multicollinearity and overdisception occurs. To overcome multicollinearity, variable selection is performed. Based on the measurement of the goodness of the model using AIC, the GPR model provides better accuracy than Poisson regression to model DHF in East Nusa Tenggara which is 218.5.

scholarly journals Modeling COVID-19 Cases in Nigeria Using Some Selected Count Data Regression Models

2020 ◽  
pp. 64-73
Author(s):  
Samuel Olorunfemi Adams ◽  
Muhammad Ardo Bamanga ◽  
Samuel Olayemi Olanrewaju ◽  
Haruna Umar Yahaya ◽  
Rafiu Olayinka Akano
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Significance Level ◽  
Generalized Poisson ◽  
Binomial Regression ◽  
Selection Test ◽  
Generalized Poisson Regression

COVID-19 is currently threatening countries in the world. Presently in Nigeria, there are about 29,286 confirmed cases, 11,828 discharged and 654 deaths as of 6th July 2020. It is against this background that this study was targeted at modeling daily cases of COVID-19’s deaths in Nigeria using count regression models like; Poisson Regression (PR), Negative Binomial Regression (NBR) and Generalized Poisson Regression (GPR) model. The study aim at fitting an appropriate count Regression model to the confirmed, active and critical cases of COVID-19 in Nigeria after 118 days. The data for the study was extracted from the daily COVID-19 cases update released by the Nigeria Centre for Disease Control (NCDC) online database from February 28th, 2020 – 6th, July 2020. The extracted data were used in the simulation of Poisson, Negative Binomial, and Generalized Poisson Regression model with a program written in STATA version 14 and fitted to the data at a 5% significance level. The best model was selected based on the values of -2logL, AIC, and BIC selection test/criteria. The results obtained from the analysis revealed that the Poisson regression could not capture over-dispersion, so other forms of Poisson Regression models such as the Negative Binomial Regression and Generalized Poisson Regression were used in the estimation. Of the three count Regression models, Generalized Poisson Regression was the best model for fitting daily cumulative confirmed, active and critical COVID-19 cases in Nigeria when overdispersion is present in the predictors because it had the least -2log-Likelihood, AIC, and BIC. It was also discovered that active and critical cases have a positive and significant effect on the number of COVID-19 related deaths in Nigeria.

scholarly journals Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized poisson regression model

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Mohammad Mafijul Islam ◽  
Morshed Alam ◽  
Md Tariquzaman ◽  
Mohammad Alamgir Kabir ◽  
Rokhsona Pervin ◽  
...  
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Poisson Regression Model ◽  
Under Five ◽  
Generalized Poisson ◽  
Malnourished Children ◽  
Generalized Poisson Regression

scholarly journals Efficiency of Zero-Inflated Generalized Poisson Regression Model on Hospital Length of Stay Using Real Data and Simulation Study

2018 ◽  
Vol 3 (1) ◽  
pp. 5-9 ◽  
Author(s):  
Roghaye Farhadi Hassankiadeh ◽  
Anoshirvan Kazemnejad ◽  
Mohammad Gholami Fesharaki ◽  
Siamak Kargar Jahromi ◽  
◽  
...  
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Simulation Study ◽  
Poisson Regression ◽  
Real Data ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Poisson Regression Model ◽  
Generalized Poisson ◽  
Generalized Poisson Regression
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