Comparing the Performance of Different High Dimensional Variable Selection Techniques on the Low Dimensional HIV/AIDS Data set

Abstract

Heavy censoring and high dimensionality have caused a great deal of difficulties for fitting and selection of model. This paper focuses on the performance of these four variable reduction techniques proposed by Khan and Shaw (2013) to select the variables to estimate the survival time of low dimensional HIV/ AIDS patients’ data. The techniques used are adaptive elastic net, weighted elastic net, adaptive elastic net with censoring constraints and weighted elastic net with censoring constraints. The performance of these approaches is compared among themselves along with the full model (model with all the predictors). It is observed that Adaptive Elastic Net with Censoring Constraints performed best among all the methods. Moreover, these four techniques can also be used for future prediction of survival time under AFT model. How to cite this article:Sharma A, Ravi V, Sehgal VK, Grover G. Comparing the Performance of Different High Dimensional Variable Selection Techniques on the Low Dimensional HIV/AIDS Data set. J Commun Dis 2020; 52(1): 14-21. DOI: https://doi.org/10.24321/0019.5138.202003