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Tuning paramater selection in penalized logistic regression with application in cancer
(İstanbul Ticaret Üniversitesi, 2019) Al-Khateeb, Sahar Fadhil
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.