scholarly journals The Estimation of Generalized Method Moment Poisson Regression Model on the Prevalence of Acute Respiratory Tract Infection (RTI) in South Kalimantan

CAUCHY ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 161
Author(s):  
Mahpolah Mahpolah ◽  
Suharto Suharto ◽  
Arief Wibowo ◽  
Bambang Widjanarko Otok
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Social Economy ◽  
Acute Respiratory Tract Infection ◽  
Nutrition Status ◽  
Health Clinic ◽  
Poisson Regression Model ◽  
Likelihood Estimator ◽  
Regression Approach ◽  
Important Health

<em>ACUTE (RTI)</em> is still an important health problem because the cause of the death of infants and children under five high enough, 1 from 4 death that happens. The purpose of this research examines the factors that affect the genesis <em>ACUTE (RTI)</em> using poisson regression approach with estimates of the maximum likelihood estimator (MLE) and generalized method moment (GMM). This research done in the area of Health Clinic in South Kalimantan. The results of the study showed that the estimates of the GMM method on Poisson regression model gives better performance in terms of the significance of the parameters than the MLE method. The factors that affect an increasing number of the prevalence of <em>ACUTE (RTI)</em> a region namely persentase Breast Feeding non-exclusive (0.0279), the percentage of low birth weight (0.0569), the percentage of shelter density (0.028), the percentage of the existence of smoker family members in the house (0.0308), the percentage of immunization is not complete (0.0193). While the factors that affect a downturn in the number of the prevalence of <em>ACUTE (RTI)</em> in a region which is the percentage of the number of infants less than 2 (0.0364), the percentage of normal nutrition status (0.0224), the percentage of Mothers Education on high school (0.0339), and the percentage of social economy (<em>UMP</em> enough to top) (0.0194).

scholarly journals Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 548
Author(s):  
Benedicta B. Aladeitan ◽  
Olukayode Adebimpe ◽  
Adewale F. Lukman ◽  
Olajumoke Oludoun ◽  
Oluwakemi E. Abiodun
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Generalized Linear Model ◽  
Real Life ◽  
Poisson Regression Model ◽  
Likelihood Estimator ◽  
Ridge Estimator ◽  
Liu Estimator ◽  
Life Study ◽  
Ridge Regression Estimator

Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the Kibria-Lukman (KL) estimator, though literature shows that the KL estimator is preferred. Therefore, this study sought to modify the KL estimator to mitigate the Poisson Regression Model with multicollinearity. Methods: A simulation study and a real-life study were carried out and the performance of the new estimator was compared with some of the existing estimators. Results: The simulation result showed the new estimator performed more efficiently than the MLE, Poisson Ridge Regression Estimator (PRE), Poisson Liu Estimator (PLE) and the Poisson KL (PKL) estimators. The real-life application also agreed with the simulation result. Conclusions: In general, the new estimator performed more efficiently than the MLE, PRE, PLE and the PKL when multicollinearity was present.

scholarly journals Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 548
Author(s):  
Benedicta B. Aladeitan ◽  
Olukayode Adebimpe ◽  
Adewale F. Lukman ◽  
Olajumoke Oludoun ◽  
Oluwakemi E. Abiodun
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Generalized Linear Model ◽  
Real Life ◽  
Poisson Regression Model ◽  
Likelihood Estimator ◽  
Ridge Estimator ◽  
Liu Estimator ◽  
Life Study ◽  
Ridge Regression Estimator

Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the Kibria-Lukman (KL) estimator, though literature shows that the KL estimator is preferred. Therefore, this study sought to modify the KL estimator to mitigate the Poisson Regression Model with multicollinearity. Methods: A simulation study and a real-life study was carried out and the performance of the new estimator was compared with some of the existing estimators. Results: The simulation result showed the new estimator performed more efficiently than the MLE, Poisson Ridge Regression Estimator (PRE), Poisson Liu Estimator (PLE) and the Poisson KL (PKL) estimators. The real-life application also agreed with the simulation result. Conclusions: In general, the new estimator performed more efficiently than the MLE, PRE, PLE and the PKL when multicollinearity was present.

scholarly journals The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huihui Zhang ◽  
Yini Liu ◽  
Fangyao Chen ◽  
Baibing Mi ◽  
Lingxia Zeng ◽  
...  
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Linear Models ◽  
Intervention Strategy ◽  
Health Resources ◽  
Sociodemographic Factors ◽  
Information Criteria ◽  
Poisson Regression Model ◽  
Geographically Weighted Regression Model ◽  
Auto Correlation

Abstract Background Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective. Methods Official surveillance data about the COVID-19 and sociodemographic information in China’s 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis. Results Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran’s I = − 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased. Conclusions There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.

A multivariate Poisson regression model for count data

2021 ◽  
pp. 1-17
Author(s):  
J. M. Muñoz-Pichardo ◽  
R. Pino-Mejías ◽  
J. García-Heras ◽  
F. Ruiz-Muñoz ◽  
M. Luz González-Regalado
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Poisson Regression Model

Monitoring social networks based on Zero-inflated Poisson regression model

2021 ◽  
pp. 1-17
Author(s):  
Narges Motalebi ◽  
Mohammad Saleh Owlia ◽  
Amirhossein Amiri ◽  
Mohammad Saber Fallahnezhad
Keyword(s):  
Social Networks ◽  
Regression Model ◽  
Poisson Regression ◽  
Poisson Regression Model

scholarly journals Age and Sex Specific Trends in Incidence of Juvenile Idiopathic Arthritis in Danish Birth Cohorts from 1992 to 2002: A Nationwide Register Linkage Study

2021 ◽  
Vol 18 (16) ◽  
pp. 8331
Author(s):  
Isabel Cardoso ◽  
Peder Frederiksen ◽  
Ina Olmer Specht ◽  
Mina Nicole Händel ◽  
Fanney Thorsteinsdottir ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Regression Model ◽  
Incidence Rate ◽  
Poisson Regression ◽  
Linkage Study ◽  
Incidence Rates ◽  
Age Group ◽  
Poisson Regression Model ◽  
Birth Cohorts ◽  
Age And Sex

This study reports age- and sex-specific incidence rates of juvenile idiopathic arthritis (JIA) in complete Danish birth cohorts from 1992 through 2002. Data were obtained from the Danish registries. All persons born in Denmark, from 1992–2002, were followed from birth and until either the date of first diagnosis recording, death, emigration, 16th birthday or administrative censoring (17 May 2017), whichever came first. The number of incident JIA cases and its incidence rate (per 100,000 person-years) were calculated within sex and age group for each of the birth cohorts. A multiplicative Poisson regression model was used to analyze the variation in the incidence rates by age and year of birth for boys and girls separately. The overall incidence of JIA was 24.1 (23.6–24.5) per 100,000 person-years. The rate per 100,000 person-years was higher among girls (29.9 (29.2–30.7)) than among boys (18.5 (18.0–19.1)). There were no evident peaks for any age group at diagnosis for boys but for girls two small peaks appeared at ages 0–5 years and 12–15 years. This study showed that the incidence rates of JIA in Denmark were higher for girls than for boys and remained stable over the observed period for both sexes.

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.

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