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...

In this article we discuss variable selection for decision making with focus on decisions regarding when to provide treatment and which treatment to provide. Current variable selection techniques were developed for use in a supervised learning setting where the goal is prediction of the response. These techniques often downplay the importance of interaction variables that have small predictive ...

Dimension reduction and variable selection play important roles in high dimensional data analysis. The sparse MAVE, a model-free variable selection method, is a nice combination of shrinkage estimation, Lasso, and an effective dimension reduction method,MAVE (minimum average variance estimation). However, it is not robust to outliers in the dependent variable because of the use of least-squares...

One of the important components of a local search strategy for satisfiability testing is the variable selection heuristic, which determines the next variable to be flipped. In a greedy local search such as GSAT, the major decision in variable selection is the strategy for breaking ties between variables that offer the same improvement in the number of unsatisfied clauses. In this paper, we anal...

In this article we highlight some interesting facts about Bayesian variable selection methods for linear regression models in settings where the design matrix exhibits strong collinearity. We first demonstrate via real data analysis and simulation studies that summaries of the posterior distribution based on marginal and joint distributions may give conflicting results for assessing the importa...

In linear regression problems with related predictors, it is desirable to do variable selection and estimation by maintaining the hierarchical or structural relationships among predictors. In this paper, we propose nonnegative garrote methods that can naturally incorporate such relationships defined through effect heredity principles or marginality principles. We show that the methods are very ...

In this paper, a global methodology for variable selection is presented. This methodology is optimizing the Nonparametric Noise Estimation (NNE) provided by Delta Test. The 3 steps of the methodology are Variable Selection (VS), Scaling and Projection. The methodology is applies to two examples: the Boston Housing database and a financial data set. It is shown that the proposed methodology prov...

The importance of juice beverages in daily food habits makes juice authentication an important issue, for example, to avoid fraudulent practices. A successful classification model should address two important cornerstones of the quality control of juicebased beverages: to monitor the amount of juice and to monitor the amount (and nature) of other substances added to the beverages. Particularly,...

This chapter reviews methods for selecting empirically relevant predictors from a set of N potentially relevant ones for the purpose of forecasting a scalar time series. I first discuss criterion based procedures in the conventional case when N is small relative to the sample size, T . I then turn to the large N case. Regularization and dimension reduction methods are then discussed. Irrespecti...

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...