It is known that the Thresholded Lasso (TL), SCAD or MCP correct intrinsic estimation bias of Lasso. In this paper we propose an alternative method improving for predictive models with general convex loss functions which encompass normal linear models, logistic regression, quantile support vector machines. For a given penalty order absolute values nonzero coefficients and then select final mode...

This paper proposes a new approach to solve the problem of lack information in rating data due users or items, there is too little user for items collaborative filtering recommendation models (CFR models). In this approach, we consider similarity between based on lasso regression build CFR models. commonly used models, results are built only feedback matrix users. The our model predicted two ca...

2 3 Preface Many classification procedures are based on variable selection methodologies. This master thesis concentrates on continuous variable selection procedures based on the shrinkage principle. Generally, we would like to find sparse prediction rules for multi-class classification problems such that in increases the prediction accuracy but also the interpretability of the obtained predict...

BACKGROUND Because multiple loci control complex diseases, there is great interest in testing markers simultaneously instead of one by one. In this paper, we applied two model selection algorithms: the stochastic search variable selection (SSVS) and the least absolute shrinkage and selection operator (LASSO) to two quantitative phenotypes related to rheumatoid arthritis (RA). RESULTS The Gene...

Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such models, but usually involves a computationally heavy combinatorial search. Lasso (Tibshirani, 1996) is now being used as a computationally feasible alternative to model selection. Therefore it is important to study La...

In the sparse linear regression setting, we consider testing the significance of the predictor variable that enters the current lasso model, in the sequence of models visited along the lasso solution path. We propose a simple test statistic based on lasso fitted values, called the covariance test statistic, and show that when the true model is linear, this statistic has an Exp(1) asymptotic dis...

I briefly report on some unexpected results that I obtained when optimizing the model parameters of the Lasso. In simulations with varying observations-to-variables ratio n/p, I typically observe a strong peak in the test error curve at the transition point n/p = 1. This peaking phenomenon is well-documented in scenarios that involve the inversion of the sample covariance matrix, and as I illus...

I use the adaptive elastic net in a Bayesian framework and test its forecasting performance against lasso, adaptive lasso and elastic net (all used in a Bayesian framework) in a series of simulations, as well as in an empirical exercise for macroeconomic Euro area data. The results suggest that elastic net is the best model among the four Bayesian methods considered. Adaptive lasso, on the othe...