A Study of Count Regression Models for Mortality Rate
This paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data. The method used is comparing the values of Akaike Information Criterion...
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| Format: | Article |
| Language: | English |
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Mathematics Department UIN Maulana Malik Ibrahim Malang
2021-11-01
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| Series: | Cauchy: Jurnal Matematika Murni dan Aplikasi |
| Subjects: | |
| Online Access: | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/13642 |
| _version_ | 1818200567522000896 |
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| author | Anwar Fitrianto |
| author_facet | Anwar Fitrianto |
| author_sort | Anwar Fitrianto |
| collection | DOAJ |
| description | This paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data. The method used is comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to find out which method suits the data the most. The results show that the data indeed display higher variability. Among the three models, the model preferred is NEGBIN 1 model. |
| first_indexed | 2024-12-12T02:39:43Z |
| format | Article |
| id | doaj.art-407e80ce451b452691ca26af31bc20ca |
| institution | Directory Open Access Journal |
| issn | 2086-0382 2477-3344 |
| language | English |
| last_indexed | 2024-12-12T02:39:43Z |
| publishDate | 2021-11-01 |
| publisher | Mathematics Department UIN Maulana Malik Ibrahim Malang |
| record_format | Article |
| series | Cauchy: Jurnal Matematika Murni dan Aplikasi |
| spelling | doaj.art-407e80ce451b452691ca26af31bc20ca2022-12-22T00:41:11ZengMathematics Department UIN Maulana Malik Ibrahim MalangCauchy: Jurnal Matematika Murni dan Aplikasi2086-03822477-33442021-11-017114215110.18860/ca.v7i1.136425880A Study of Count Regression Models for Mortality RateAnwar Fitrianto0Department of Statistics, IPB UniversityThis paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data. The method used is comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to find out which method suits the data the most. The results show that the data indeed display higher variability. Among the three models, the model preferred is NEGBIN 1 model.https://ejournal.uin-malang.ac.id/index.php/Math/article/view/13642mortality, poisson, regression, binomial, overdispersion |
| spellingShingle | Anwar Fitrianto A Study of Count Regression Models for Mortality Rate Cauchy: Jurnal Matematika Murni dan Aplikasi mortality, poisson, regression, binomial, overdispersion |
| title | A Study of Count Regression Models for Mortality Rate |
| title_full | A Study of Count Regression Models for Mortality Rate |
| title_fullStr | A Study of Count Regression Models for Mortality Rate |
| title_full_unstemmed | A Study of Count Regression Models for Mortality Rate |
| title_short | A Study of Count Regression Models for Mortality Rate |
| title_sort | study of count regression models for mortality rate |
| topic | mortality, poisson, regression, binomial, overdispersion |
| url | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/13642 |
| work_keys_str_mv | AT anwarfitrianto astudyofcountregressionmodelsformortalityrate AT anwarfitrianto studyofcountregressionmodelsformortalityrate |