BAYESIAN ANALYSIS OF TOBIT QUANTILE REGRESSION WITH ADAPTIVE LASSO PENALTY IN HOUSEHOLD EXPENDITURE FOR CIGARETTE CONSUMPTION

Tobit Quantile Regression with Adaptive Lasso Penalty is a quantile regression model on censored data that adds Lasso's adaptive penalty to its parameter estimation. The estimation of the parameters solved by Bayesian analysis. Parameters are assumed follow certain distribution called prior distribution. Using sample information along distribution, conditional posterior searched using Box-Tiao rule. Computational solutions MCMC Gibbs Sampling algorithm. can generate samples based each in order obtain joint was applied Household Expenditure for Cigarette Consumption 2011. As comparison analysis, used. results analysis show better than Regression.