TUNING PARAMATER SELECTION IN PENALIZED LOGISTIC REGRESSION WITH APPLICATION IN CANCER
Abstract
Variable selection is an important subject in regression analysis intended to select the best subset of
predictors. In cancer classification, gene selection plays an important issue. The Least Absolute Shrinkage and
Selection Operator (LASSO) is one of most used penalized method. In logistic regression, Lasso right the
traditional parameter estimation method, maximum log-likelihood, by adding the L1-norm of the parameters to
the negative log-likelihood function. Lasso depends on the tuning parameter. Finding the optimal value for the
tuning parameter is one of the most important topics. There are three popular methods to select the optimal
value of the tuning parameter: Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), and
Cross-Validation (CV). The aim of this paper is to evaluate and compare these three methods for selecting the
optimal value of tuning parameter in terms of coefficients estimation accuracy and variable selection through
simulation studies and application in cancer classification.
Keywords
References
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Details
Primary Language
English
Subjects
Engineering
Journal Section
Research Article
Authors
Publication Date
December 31, 2019
Submission Date
November 7, 2019
Acceptance Date
July 9, 2020
Published in Issue
Year 2019 Volume: 18 Number: 36
