Regularisation in discrete survival models: A comparison of lasso and gradient boosting

Authors

  • Alphonce Bere Department of Statistics, University of Venda, Thohoyandou, South Africa
  • Godfrey H. Sithuba Department of Statistics, University of Venda, Thohoyandou, South Africa
  • Coster Mabvuu Department of Statistics, University of Venda, Thohoyandou, South Africa
  • Retang Mashabela Department of Statistics, University of Venda, Thohoyandou, South Africa
  • Caston Sigauke Department of Statistics, University of Venda, Thohoyandou, South Africa
  • Kwabena Kyei Department of Statistics, University of Venda, Thohoyandou, South Africa

DOI:

https://doi.org/10.37920/sasj.2021.55.1.3

Keywords:

Boosting, Discrete survival model, First alcohol intake, Penalized likelihood, Variable selection

Abstract

We present the results of a simulation study performed to compare the accuracy of a lassotype penalization method and gradient boosting in estimating the baseline hazard function and covariate parameters in discrete survival models. The mean square error results reveal that the lasso-type algorithm performs better in recovering the baseline hazard and covariate parameters. In particular, gradient boosting underestimates the sizes of the parameters and also has a high false positive rate. Similar results are obtained in an application to real-life data.

Downloads

Download data is not yet available.

Downloads

Published

2021-03-31

Issue

Vol. 55 No. 1 (2021)

Section

Research Articles