We use least absolute shrinkage and selection operator (LASSO) regression to select genetic markers and phenotypic features that are most informative with respect to a trait of interest. We compare several strategies for applying LASSO methods in risk prediction models, using the Genetic Analysis Workshop 17 exome simulation data consisting of 697 individuals with information on genotypic and p...

Nearest shrunken centroids (NSC) is a popular classification method for microarray data. NSC calculates centroids for each class and "shrinks" the centroids toward 0 using soft thresholding. Future observations are then assigned to the class with the minimum distance between the observation and the (shrunken) centroid. Under certain conditions the soft shrinkage used by NSC is equivalent to a L...

Variable selection in genome-wide association studies can be a daunting task and statistically challenging because there are more variables than subjects. We propose an approach that uses principal-component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) to identify gene-gene interaction in genome-wide association studies. A PCA was used to first reduce the dimension...

We consider the problem of variables selection and estimation in linear regression model in situations where the number of parameters diverges with the sample size. We propose the adaptive Generalized Ridge-Lasso (AdaGril) which is an extension of the the adaptive Elastic Net. AdaGril incorporates information redundancy among correlated variables for model selection and estimation. It combines ...

Abstract In this work, we propose an extension of the versatile joint regression framework for bivariate count responses package by Marra and Radice (R version 0.2-3, 2020) incorporating (adaptive) LASSO-type penalty. The underlying estimation algorithm is based on a quadratic approximation method enables variable selection corresponding estimates guarantee shrinkage sparsity. Hence, approach p...

Professors McShane and Wyner have written a thought-provoking paper that intends to challenge some of the conventional wisdom in the paleoclimate literature. Rather than commenting on the merits of the entire methodology we focus on one topic. Namely, we discuss theoretical and practical aspects of the use of the least absolute shrinkage and selection operator [Tibshirani (1996)], more popularl...

Despite the widespread use of liver transplantation as a routine therapy in liver diseases, the effective factors on its outcomes are still controversial. This study attempted to identify the most effective factors on death after liver transplantation. For this purpose, modified least absolute shrinkage and selection operator (LASSO), called Adaptive LASSO, was utilized. One of the best advanta...

The possibility of improving on the usual multivariate normal confidence was first discussed in Stein (1962). Using the ideas of shrinkage, through Bayesian and empirical Bayesian arguments, domination results, both analytic and numerical, have been obtained. Here we trace some of the developments in confidence set estimation.

We construct an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise, using the local and occupation times of Gaussian processes. The method relies on the almost-sure minimization of a Stein Unbiased Risk Estimator (SURE) obtained through integration by parts on Gaussian space, and applied to shrinkage estimators which are constructed by soft and...