A. Bere, Godfrey H. Sithuba, Coster Mabvuu, Retang Mashabela, C. Sigauke, K. Kyei
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引用次数: 0
Abstract
We present the results of a simulation study performed to compare the accuracy of a lasso-type 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.
期刊介绍：
The journal will publish innovative contributions to the theory and application of statistics. Authoritative review articles on topics of general interest which are not readily accessible in a coherent form, will be also be considered for publication. Articles on applications or of a general nature will be published in separate sections and an author should indicate which of these sections an article is intended for. An applications article should normally consist of the analysis of actual data and need not necessarily contain new theory. The data should be made available with the article but need not necessarily be part of it.
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