Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach

Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables “number of blood donation” and “number of blood deferral”: as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models.

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

BMC Infectious Diseases ◽

10.1186/s12879-021-06128-1 ◽

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.

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PARAMETER ESTIMATION ON HURDLE POISSON REGRESSION MODEL WITH CENSORED DATA

Jurnal Teknologi ◽

10.11113/jt.v57.1533 ◽

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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The Analysis of Influencing Factors on the Number of Storms

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.166-169.2649 ◽

2012 ◽

Vol 166-169 ◽

pp. 2649-2653

Author(s):

Bin Hui Wang ◽

Zhi Jian Wang ◽

Si Ling Chen

Keyword(s):

Regression Model ◽

Influencing Factors ◽

Poisson Regression ◽

West African ◽

The Other ◽

Poisson Regression Model ◽

Probability Of Occurrence ◽

Wet Weather ◽

The Impact ◽

Non Parametric

By using both parametric and non-parametric tests, noticed that both EI Nino and West African Wetness have significant impact on the number of storms. A Poisson Regression Model is then be used to further explores the impact of different variables to the number of storms. In particular, warm phase of EI Nino and dry weather has suppress impact on the number of storms while cold phase of Nino and wet weather encourage storms. Under the combination impact of both EI Nino and West African Wetness, the probability of occurrence of extreme storms is higher than under the other conditions.

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Prediction of the Effect of Demographic Characteristics on Parity Using Poisson Regression Model

Asian Journal of Probability and Statistics ◽

10.9734/ajpas/2020/v6i130154 ◽

2020 ◽

pp. 55-63

Author(s):

C. M. Gatwiri ◽

M. M. Muraya ◽

L. K. Gitonga

Keyword(s):

Regression Model ◽

Marital Status ◽

Poisson Regression ◽

Regression Models ◽

Poisson Model ◽

Demographic Characteristics ◽

Ratio Test ◽

Poisson Regression Model ◽

Individual Character ◽

Dispersion Test

There is growing interest among the public in demography since demographic change has become the subject of political debates in many countries. Statistics on demography are used to support policy-making and monitor demographic behaviour of political, economic, social and cultural perspectives. Most studies have used descriptive statistics to study demographic characteristics. Moreover, most of these studies investigate effects of individual character at a time. Therefore, there is a need to come up with more robust statistical methods, such as predictive models for demographic studies. The objective of this study was to predict the effect of demographic characteristics on parity using Poisson regression model. Secondary data on parity, age, marital status and education level was collected from Chuka and Embu hospital maternal units from 2013 to 2017. The data was analysed using R-statistical software. Three Poisson regression models (PRMs) were fitted. The likelihood ratio test of all the Poisson regression models had p-values < 0.05 indicating that all the models were statistically significant. Deviance test and Akaike Information Criterion (AIC) were used to assess the fit of Poisson regression models. The overall Poisson model had residual deviance of 184.23, which was the lowest of all other fitted PRM models, suggesting that it was the best fit. The AIC of the PRM with both education and marital status as the predictors had the lowest AIC value of 2078.620, indicating that it was the best fitted model. The dispersion test proved that PRM was not over-dispersed, confirming the model as a good fit of the data. The improved model can be used in prediction of population growth rates.

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Metode Pemilihan Variabel pada Model Regresi Poisson Menggunakan Metode Nordberg

Jurnal Riset Statistika ◽

10.29313/jrs.v1i1.24 ◽

2021 ◽

Vol 1 (1) ◽

pp. 35-42

Author(s):

Muhammad Bangkit Riksa Utama ◽

Nusar Hajarisman

Keyword(s):

Regression Model ◽

Linear Model ◽

Poisson Regression ◽

Generalized Linear Model ◽

Information Criteria ◽

Infant Mortality Rate ◽

Poisson Regression Model ◽

Independent Variables ◽

Selection Of Variables ◽

Selection Of

Abstract. In various experiments, data interactions take the form of discrete numbers or counts. The model that can be used for these data is the Poisson regression model. Poisson regression is included in the Generalized Linear Model (GLM). Poisson regression in general is very important in various fields and agreed to receive special attention. Often this model needs many independent variables. Then there needs to be a selection of poisson regression model variables. Due to the number of independent variables that exist, the selection of variables is carried out. Variable selection techniques that are commonly known are the forward, backward method, akaike information criteria and several other methods. In this paper, we will discuss one method of selecting variables in the Poisson regression model that has been made in the algorithm created by Famoye and Rothe. The algorithm created will be compared with the algorithm made by Nordberg. In this study data were used on Infant Mortality Rate (IMR) in West Java Province. Abstrak. Dalam berbagai eksperimen, seringkali data berupa bilangan diskrit atau cacah. Model yang dapat digunakan untuk data tersebut diantaranya adalah model regresi poisson. Regresi poisson termasuk kedalam Generalized Linear Model (GLM). Regresi poisson secara umum sangat penting dalam berbagai bidang dan karenanya patut mendapat perhatian khusus. Seringkali model ini melibatkan banyak variabel independen. Maka perlu adanya cara untuk mempertimbangkan pemilihan variabel model regresi poisson. Dikarenakan banyaknya variabel independen yang ada maka dilakukan penyeleksian variabel. Teknik pemilihan variabel yang sudah biasa dikenal yaitu metode forward, backward, akaike information criterion dan beberapa metode lainnya. Pada makalah ini akan dibahas mengenai salah satu metode pemilihan variabel dalam model regresi poisson yang telah dibentuk dalam algoritma yang dibuat oleh Famoye dan Rothe. Algortitma yang dibuat ini akan dibandingkan dengan algoritma yang telah dibuat oleh Nordberg. Pada penelitian ini digunakan data mengenai Angka Kematian Bayi (AKB) di Provinsi Jawa Barat.

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The Exponential-Poisson Regression Model for Recurrent Events: A Bayesian Approach

Springer Proceedings in Mathematics & Statistics - Interdisciplinary Bayesian Statistics ◽

10.1007/978-3-319-12454-4_29 ◽

2015 ◽

pp. 347-356 ◽

Cited By ~ 1

Author(s):

Márcia A. C. Macera ◽

Francisco Louzada ◽

Vicente G. Cancho

Keyword(s):

Regression Model ◽

Bayesian Approach ◽

Poisson Regression ◽

Recurrent Events ◽

Poisson Regression Model

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Analysis of The Mixed Effects Regression Model for Clustered Count Response Data

10.21203/rs.3.rs-157733/v1 ◽

2021 ◽

Author(s):

Aragaw Eshetie Aguade ◽

B.Muniswamy Begari

Keyword(s):

Drinking Water ◽

Regression Model ◽

Count Data ◽

Data Model ◽

Poisson Regression ◽

Score Test ◽

Poisson Model ◽

Poisson Regression Model ◽

Response Data ◽

Count Response

Abstract BackgroundThe Poisson regression model is useful for analyse count data, but, when the observations are correlated the Poisson estimate will be biased. Whereas, when the over-dispersion and heterogeneity problems occur the imposition of the Poisson model underestimate the standard error and overestimate the significance of the regression parameters. Therefore, the objective of this paper was to develop a test statistic to model and predict clustered count response data via the application and simulation data.MethodsThis paper concentrated on the clustered count data model to take into account heterogeneity. Accordingly, we developed a score test based on the multilevel Poisson model for testing heterogeneity with the alternative Poisson regression model. In addition, for the model application, we used the EDHS children`s data. Therefore, to evaluate the proposed model, we used both simulation and application data.ResultsSimulation results showed that the proposed score test has high power to predict and used to control heterogeneity between groups. Oromia, Amhara, and SNNPR are among the regions with the highest child mortality rates (Table 1). The results indicated that women who made marriage a mean age of 16 years and gave birth to the first child a mean age of 18 years and 8 months. Table 1 showed that 81% of all child deaths have recorded in rural areas. 78% of child families were illiterate, as a result, 75% of children don't have access to latrines and drinking water. Rivers and open-source waters are the common sources of drinking water, which comprised 79% of the total water supply. Therefore, from the research finding, it is possible to conclude that most child mortality is due to scarcity of water.ConclusionThe Power of test estimates indicated that the proposed method was better than the existing models. All covariant and dummy explanatory variables have a significant effect on the deaths of children. Hence, the multilevel Poisson model results indicated that there exists high variability among regions for the deaths of children. Therefore, this work suggested that the applications of the random-effects model provided a simple and robust means to predict the count response data model.

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Analysis of the Mixed Effects Regression Model for Clustered Count Response Data

10.21203/rs.3.rs-70873/v1 ◽

2020 ◽

Author(s):

Aragaw Eshetie Aguade ◽

Muniswamy Begari

Keyword(s):

Drinking Water ◽

Regression Model ◽

Count Data ◽

Data Model ◽

Poisson Regression ◽

Score Test ◽

Poisson Model ◽

Poisson Regression Model ◽

Response Data ◽

Count Response

Abstract Background The Poisson regression model is useful to analyze count data, but, when the observations are correlated the Poisson estimate will be biased. Whereas, when the over-dispersion and heterogeneity problems occur the imposition of the Poisson model underestimate the standard error and overestimate the significance of the regression parameters. Therefore, the objective of this paper was to develop a test statistic to model and predict clustered count response data via application and simulation data. Methods This paper concentrated on the clustered count data model to take into account heterogeneity. Accordingly, we developed a score test based on the multilevel Poisson model for testing heterogeneity with the alternative Poisson regression model. In addition, for the model application, we used the EDHS children`s data. Therefore, to evaluate the proposed model, we used both simulation and application data. Results Simulation results showed that the proposed score test has high power to predict and used to control heterogeneity between groups. Oromia, Amhara, and SNNPR are among the regions with the highest child mortality rates (Table 1). The results indicated that women who made marriage a mean age of 16 years and gave birth for the first child a mean age of 18 years and 8 months. Table 1 showed that 81% of all child deaths have recorded in rural areas. 78% of child families were illiterate, as a result, 75% of children don't have access to latrines and drinking water. Rivers and open-sources waters are the common sources of drinking water which comprised 79% of the total water supply. Therefore, from the research finding, it is possible to conclude that most child mortality is due to scarcity of water. Conclusion The Power of test estimates indicated that the proposed method was better than the existing models. All covariant and dummy explanatory variables have a significant effect on the deaths of children. Hence, the multilevel Poisson model results indicated that there exist high variability among regions for the deaths of children. Therefore, this work suggested that the applications of the random-effects model provided a simple and robust means to predict the count response data model.

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