The Bayesian LASSO (BL) has been pointed out to be an effective approach to sparse model representation and successfully applied to quantitative trait loci (QTL) mapping and genomic breeding value (GBV) estimation using genome-wide dense sets of markers. However, the BL relies on a single parameter known as the regularization parameter to simultaneously control the overall model sparsity and th...

Our goal is to identify common single-nucleotide polymorphisms (SNPs) (minor allele frequency > 1%) that add predictive accuracy above that gained by knowledge of easily measured clinical variables. We take an algorithmic approach to predict each phenotypic variable using a combination of phenotypic and genotypic predictors. We perform our procedure on the first simulated replicate and then val...

Abstract: The problem of estimating the centre of symmetry of an unknown periodic function observed in Gaussian white noise is considered. Using the penalized blockwise Stein method, a smoothing filter allowing to define the penalized profile likelihood is proposed. The estimator of the centre of symmetry is then the maximizer of this penalized profile likelihood. This estimator is shown to be ...

New challenges have arisen with the development of large marker panels for livestock species. Models easily become overparameterized when all available markers are included. Solutions have led to the development of shrinkage or regularization techniques. The objective of this study was the application and comparison of Bayesian LASSO (B-L), thick-tailed (Student-t), and semiparametric multiple ...

We propose the elastic net, a new regression shrinkage and selection method. Real data and a simulation study show that the elastic net often outperforms the lasso, while it enjoys a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strong correlated predictors are kept in the model. The elastic net is particularly useful in the analysis of mic...

This chapter reviews the literature on variable selection in nonparametric and semiparametric regression models via shrinkage. We highlight recent developments on simultaneous variable selection and estimation through the methods of least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD) or their variants, but restrict our attention to nonparametric a...

1 Summary. The additive risk model is a useful alternative to the proportional hazards model. It postulates that the hazard function is the sum of the baseline hazard function and the regression function of covariates. In this article, we investigate estimation in the additive risk model with right censored survival data and high dimensional covariates. A LASSO (least absolute shrinkage and sel...

Penalized linear regression methods are used for the accurate prediction of new observations and to obtain interpretable models. The performance these depends on properties true coefficient vector. LASSO method is a penalized that can simultaneously perform shrinkage variable selection in continuous process. Depending structure dataset, different estimators have been proposed overcome problems ...

The Dantzig selector performs variable selection and model fitting in linear regression. It uses an L1 penalty to shrink the regression coefficients towards zero, in a similar fashion to the Lasso. While both the Lasso and Dantzig selector potentially do a good job of selecting the correct variables, they tend to over-shrink the final coefficients. This results in an unfortunate trade-off. One ...

Mutual fund selection is a notoriously difficult task, because past performance poor predictor of future performance. We propose measure that incorporates simple idea: shrinkage, in the sense Bayes-James-Stein, should be applied to gross return parameters, but not fees, which are known. The proposed Shrinkage Adjusted Sharpe ratio (SAS) substantially improves prediction out-of-sample relative e...