Extending the GLM Framework of the Lee-Carter Model with Random Forest Recursive Feature Elimination Based Determinants of Mortality; [Perluasan Model Kerangka GLM Lee-Carter dengan Faktor Penyebab Kematian Berdasarkan Eliminasi Ciri Rekursif Hutan Rawak]

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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Published in:Sains Malaysiana
Main Author: Yaacob N.A.; Pathmanathan D.; Mohamed I.
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139065470&doi=10.17576%2fjsm-2022-5107-24&partnerID=40&md5=fa0059855f9eb1138613d9791a60f9b8
id 2-s2.0-85139065470
spelling 2-s2.0-85139065470
Yaacob N.A.; Pathmanathan D.; Mohamed I.
Extending the GLM Framework of the Lee-Carter Model with Random Forest Recursive Feature Elimination Based Determinants of Mortality; [Perluasan Model Kerangka GLM Lee-Carter dengan Faktor Penyebab Kematian Berdasarkan Eliminasi Ciri Rekursif Hutan Rawak]
2022
Sains Malaysiana
51
7
10.17576/jsm-2022-5107-24
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139065470&doi=10.17576%2fjsm-2022-5107-24&partnerID=40&md5=fa0059855f9eb1138613d9791a60f9b8
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. © 2022 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.
Penerbit Universiti Kebangsaan Malaysia
1266039
English
Article
All Open Access; Gold Open Access
author Yaacob N.A.; Pathmanathan D.; Mohamed I.
spellingShingle Yaacob N.A.; Pathmanathan D.; Mohamed I.
Extending the GLM Framework of the Lee-Carter Model with Random Forest Recursive Feature Elimination Based Determinants of Mortality; [Perluasan Model Kerangka GLM Lee-Carter dengan Faktor Penyebab Kematian Berdasarkan Eliminasi Ciri Rekursif Hutan Rawak]
author_facet Yaacob N.A.; Pathmanathan D.; Mohamed I.
author_sort Yaacob N.A.; Pathmanathan D.; Mohamed I.
title Extending the GLM Framework of the Lee-Carter Model with Random Forest Recursive Feature Elimination Based Determinants of Mortality; [Perluasan Model Kerangka GLM Lee-Carter dengan Faktor Penyebab Kematian Berdasarkan Eliminasi Ciri Rekursif Hutan Rawak]
title_short Extending the GLM Framework of the Lee-Carter Model with Random Forest Recursive Feature Elimination Based Determinants of Mortality; [Perluasan Model Kerangka GLM Lee-Carter dengan Faktor Penyebab Kematian Berdasarkan Eliminasi Ciri Rekursif Hutan Rawak]
title_full Extending the GLM Framework of the Lee-Carter Model with Random Forest Recursive Feature Elimination Based Determinants of Mortality; [Perluasan Model Kerangka GLM Lee-Carter dengan Faktor Penyebab Kematian Berdasarkan Eliminasi Ciri Rekursif Hutan Rawak]
title_fullStr Extending the GLM Framework of the Lee-Carter Model with Random Forest Recursive Feature Elimination Based Determinants of Mortality; [Perluasan Model Kerangka GLM Lee-Carter dengan Faktor Penyebab Kematian Berdasarkan Eliminasi Ciri Rekursif Hutan Rawak]
title_full_unstemmed Extending the GLM Framework of the Lee-Carter Model with Random Forest Recursive Feature Elimination Based Determinants of Mortality; [Perluasan Model Kerangka GLM Lee-Carter dengan Faktor Penyebab Kematian Berdasarkan Eliminasi Ciri Rekursif Hutan Rawak]
title_sort Extending the GLM Framework of the Lee-Carter Model with Random Forest Recursive Feature Elimination Based Determinants of Mortality; [Perluasan Model Kerangka GLM Lee-Carter dengan Faktor Penyebab Kematian Berdasarkan Eliminasi Ciri Rekursif Hutan Rawak]
publishDate 2022
container_title Sains Malaysiana
container_volume 51
container_issue 7
doi_str_mv 10.17576/jsm-2022-5107-24
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139065470&doi=10.17576%2fjsm-2022-5107-24&partnerID=40&md5=fa0059855f9eb1138613d9791a60f9b8
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. © 2022 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.
publisher Penerbit Universiti Kebangsaan Malaysia
issn 1266039
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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