Title: Big Data Analysis Using Shrinkage Strategies,
Abstract: In this paper, we apply shrinkage strategies to estimate regression
coefficients efficiently for the high-dimensional multiple regression model,
where the number of samples is smaller than the number of predictors. We assume
in the sparse linear model some of the predictors have very weak influence on
the response of interest. We propose to shrink estimators more than usual.
Specifically, we use integrated estimation strategies in sub and full models
and shrink the integrated estimators by incorporating a bounded measurable
function of some weights. The exhibited double shrunken estimators improve the
prediction performance of sub models significantly selected from existing
Lasso-type variable selection methods. Monte Carlo simulation studies as well
as real examples of eye data and Riboavin data confirm the superior performance
of the estimators in the high-dimensional regression model.