scholarly journals Metode Pemilihan Variabel pada Model Regresi Poisson Menggunakan Metode Nordberg

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.

scholarly journals Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: An application to the FIFA World Cup 2014

2015 ◽  
Vol 11 (2) ◽  
Author(s):  
Andreas Groll ◽  
Gunther Schauberger ◽  
Gerhard Tutz
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Linear Model ◽  
Poisson Regression ◽  
Generalized Linear Model ◽  
Poisson Regression Model ◽  
World Cup ◽  
Fifa World Cup ◽  
Specific Effects ◽  
World Champion

AbstractIn this article an approach for the analysis and prediction of international soccer match results is proposed. It is based on a regularized Poisson regression model that includes various potentially influential covariates describing the national teams’ success in previous FIFA World Cups. Additionally, within the generalized linear model (GLM) framework, also differences of team-specific effects are incorporated. In order to achieve variable selection and shrinkage, we use tailored Lasso approaches. Based on preceding FIFA World Cups, two models for the prediction of the FIFA World Cup 2014 are fitted and investigated. Based on the model estimates, the FIFA World Cup 2014 is simulated repeatedly and winning probabilities are obtained for all teams. Both models favor the actual FIFA World Champion Germany.

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.

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 Identifikasi Faktor-faktor yang Mempengaruhi Angka Kematian Bayi di Provinsi Jawa Barat melalui Model Berbasis Regional

2016 ◽  
Vol 32 (1) ◽  
pp. 194
Author(s):  
Nusar Hajarisman ◽  
Yayat Karyana
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Regression Model ◽  
Poisson Regression ◽  
Spatial Diversity ◽  
Infant Mortality Rate ◽  
Poisson Regression Model ◽  
West Java ◽  
Infant Deaths ◽  
Geographic Modeling

In geographic modeling, global models such as ordinary linear regression (OLR) model theoretically it provides quite reliable local information if there is not any spatial diversity by region. In other words, OLR model cannot describe the relations between variables in heterogeneous difference of each region. This study will consider a model that will be used to estimate or predict the infant mortality rate in the several regencies / cities in West Java Province. Because the response variable observed in this study is count data which is assumed Poisson distributed, geographically weighted Poisson regression model (GWPR) is used. A better model is used to analyze the data of infant deaths in each regency / city in West Java based on the AIC value, GWPR model has the smallest value (compared to Poisson regression model), in which there is an interesting and important difference from each regency/city about the factors that significantly influence the Infant Mortality rate in each region.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Tayeb Mohammadi ◽  
Soleiman Kheiri ◽  
Morteza Sedehi
Keyword(s):  
Blood Transfusion ◽  
Regression Model ◽  
Bayesian Approach ◽  
Poisson Regression ◽  
Blood Donation ◽  
Poisson Model ◽  
Joint Modeling ◽  
Information Criteria ◽  
The Other ◽  
Poisson Regression Model

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.

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.

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