Selection of variables in generalized linear mixed model for smoker in Jambi Province

Abstract

Smoking is one of the health problems in Indonesia. Many factors cause a person to smoke, both originating from oneself and the environment. The statistical question that arises is how to choose the factors that are most significant in influencing people to smoke. These factors are the variables that will sed in modeling. This study aims to select the variables in the compressed linear mixed model using the Lasso penalty and the Boosting function, using the EM and REML algorithms. Respondents in this study were 160 smokers in Jambi Province. Based on the AIC value, the best model obtained from the selection of variables with the Boosting function and REML algorithm. The analysis shows that work, welfare level, and family members who smoke are the factors that influence people smoking in Jambi Province.