A simple method for subset selection of independent variables in regression models is proposed. We expand the usual regression equation to an equation that incorporates all possible subsets of predictors by adding indicator variables as parameters. The vector of indicator variables dictates which predictors to include. Several choices of priors can be employed for the unknown regression coeecie...

After its inception in Koenker and Bassett (1978), quantile regression has become an important and widely used technique to study the whole conditional distribution of a response variable and grown into an important tool of applied statistics over the last three decades. In this work, we focus on the variable selection aspect of penalized quantile regression. Under some mild conditions, we demo...

Instrumental variable (IV) estimation typically requires the user to correctly specify the relationship between the regressors and the outcome to obtain a consistent estimate of the effects of the treatments. This paper proposes doubly robust IV regression estimators that only require the user to either correctly specify the relationship between the measured confounding variables (i.e., include...

Objectives: The categorical data analysis is very important in statistics and medical sciences. When the binary response variable is misclassified, the results of fitting the model will be biased in estimating adjusted odds ratios.  The present study aimed to use a method to detect and correct misclassification error in the response variable of Type 2 Diabetes Mellitus (T2DM), applying binary ...

support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...