Extending the GLM framework of the Lee-Carter model with random forest recursive feature elimination based determinants of mortality
The Lee-Carter (LC) model led to the development of many prominent mortality models. This study aims to modify the generalised linear model (GLM) (Poisson, negative binomial, and binomial) framework of the LC model by incorporating factors that affect mortality into the model. The top three factors...
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/20251/ http://journalarticle.ukm.my/20251/1/24.pdf |
| _version_ | 1848815054345994240 |
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| author | Nurul Aityqah Yaacob, Dharini Pathmanathan, Ibrahim Mohamed, |
| author_facet | Nurul Aityqah Yaacob, Dharini Pathmanathan, Ibrahim Mohamed, |
| author_sort | Nurul Aityqah Yaacob, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | The Lee-Carter (LC) model led to the development of many prominent mortality models. This study aims to modify the generalised linear model (GLM) (Poisson, negative binomial, and binomial) framework of the LC model by incorporating factors that affect mortality into the model. The top three factors which affect the mortality for each of the 14 countries studied were selected using the random forest recursive feature elimination (RF-RFE) method which eliminates the least important factors based on the correlation of the predictors with the log-mortality rate. These selected factors were integrated in the form of additional bilinear variates to the GLM models and compared to their original counterparts. The RF-RFE method is effective in selecting the best determinants of mortality by avoiding multicollinearity among predictor variables. The inclusion of the time-factor modulation based on the factors selected improved the model adequacy significantly. Vast improvement was evident in the Poisson and binomial settings. Furthermore, the modified GLM version fits short-base-period data well. This study shows that the inclusion of exogenous determinants of mortality improves the performance of the model significantly. |
| first_indexed | 2025-11-15T00:43:53Z |
| format | Article |
| id | oai:generic.eprints.org:20251 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:43:53Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:202512022-10-25T07:54:59Z http://journalarticle.ukm.my/20251/ Extending the GLM framework of the Lee-Carter model with random forest recursive feature elimination based determinants of mortality Nurul Aityqah Yaacob, Dharini Pathmanathan, Ibrahim Mohamed, The Lee-Carter (LC) model led to the development of many prominent mortality models. This study aims to modify the generalised linear model (GLM) (Poisson, negative binomial, and binomial) framework of the LC model by incorporating factors that affect mortality into the model. The top three factors which affect the mortality for each of the 14 countries studied were selected using the random forest recursive feature elimination (RF-RFE) method which eliminates the least important factors based on the correlation of the predictors with the log-mortality rate. These selected factors were integrated in the form of additional bilinear variates to the GLM models and compared to their original counterparts. The RF-RFE method is effective in selecting the best determinants of mortality by avoiding multicollinearity among predictor variables. The inclusion of the time-factor modulation based on the factors selected improved the model adequacy significantly. Vast improvement was evident in the Poisson and binomial settings. Furthermore, the modified GLM version fits short-base-period data well. This study shows that the inclusion of exogenous determinants of mortality improves the performance of the model significantly. Penerbit Universiti Kebangsaan Malaysia 2022-07 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/20251/1/24.pdf Nurul Aityqah Yaacob, and Dharini Pathmanathan, and Ibrahim Mohamed, (2022) Extending the GLM framework of the Lee-Carter model with random forest recursive feature elimination based determinants of mortality. Sains Malaysiana, 51 (7). pp. 2237-2247. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid51bil7_2022/KandunganJilid51Bil7_2022.html |
| spellingShingle | Nurul Aityqah Yaacob, Dharini Pathmanathan, Ibrahim Mohamed, Extending the GLM framework of the Lee-Carter model with random forest recursive feature elimination based determinants of mortality |
| title | Extending the GLM framework of the Lee-Carter model with random forest recursive feature elimination based determinants of mortality |
| title_full | Extending the GLM framework of the Lee-Carter model with random forest recursive feature elimination based determinants of mortality |
| title_fullStr | Extending the GLM framework of the Lee-Carter model with random forest recursive feature elimination based determinants of mortality |
| title_full_unstemmed | Extending the GLM framework of the Lee-Carter model with random forest recursive feature elimination based determinants of mortality |
| title_short | Extending the GLM framework of the Lee-Carter model with random forest recursive feature elimination based determinants of mortality |
| title_sort | extending the glm framework of the lee-carter model with random forest recursive feature elimination based determinants of mortality |
| url | http://journalarticle.ukm.my/20251/ http://journalarticle.ukm.my/20251/ http://journalarticle.ukm.my/20251/1/24.pdf |