scholarly journals Methodology to Assess Air Pollution Impact on Human Health Using the Generalized Linear Model with Poisson Regression

10.5772/33385 ◽  
2012 ◽  
Author(s):  
Yara de Souza Tadano ◽  
Cssia Maria Lie Ugaya ◽  
Admilson Teixeira
Keyword(s):  
Air Pollution ◽  
Human Health ◽  
Linear Model ◽  
Poisson Regression ◽  
Generalized Linear Model ◽  
Pollution Impact

Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics

2020 ◽  
Vol 191 ◽  
pp. 110106 ◽  
Author(s):  
Jônatas T. Belotti ◽  
Diego S. Castanho ◽  
Lilian N. Araujo ◽  
Lucas V. da Silva ◽  
Thiago Antonini Alves ◽  
...  
Keyword(s):  
Air Pollution ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Air Pollution Epidemiology ◽  
Model Approach

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 Correlational study of air pollution-related diseases (asthma, conjunctivitis, URTI and dengue) in Johor Bahru, Malaysia

2017 ◽  
Vol 13 (4-1) ◽  
pp. 354-361 ◽  
Author(s):  
Aaishah Radziah Jamaludin ◽  
Fadhilah Yusof ◽  
Rahmah Mohd Lokoman ◽  
Zainura Zainoon Noor ◽  
Noreliza Alias ◽  
...  
Keyword(s):  
Air Pollution ◽  
Linear Model ◽  
Sulphur Dioxide ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Influential Factors ◽  
Motor Vehicles ◽  
Binomial Model ◽  
Climate Variables ◽  
Mobile Sources

Four pollution related diseases, namely asthma, conjunctivitis, URTI and dengue will be studied in terms of their trend, behaviour and association with influential factors such as air pollution and climate variables. Two methods were chosen; Poisson Generalized Linear Model and Negative Binomial Model. These methods were used to determine the association between the diseases and their influential factors. This study shows that Sulphur Dioxide (SO2) is the most abundant source that contributes to the diseases. Therefore, the local authorities such as the Department of Environment need to reinforce the law in planning and monitoring the SO2 sources which are produced from fuel combustion in mobile sources and motor vehicles. 

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.

Sign in / Sign up

Export Citation Format

Share Document