The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches ...

Improved estimation of eigen vector of covariance matrix is considered under uncertain prior information (UPI) regarding the parameter vector. Like statistical models underlying the statistical inferences to be made, the prior information will be susceptible to uncertainty and the practitioners may be reluctant to impose the additional information regarding parameters in the estimation process....

In recent years, with wide application of proton exchange membrane fuel cell (PEMFC) in vehicles and portable applications, researches regarding PEMFC lifetime behavior associated prognostic techniques receive more interest. this article, a least absolute shrinkage selection operator-echo state network (LASSO-ESN)-based strategy is proposed for the optimization input parameters long-term predic...

We develop a novel Bayesian doubly adaptive elastic-net Lasso (DAELasso) approach for VAR shrinkage. DAELasso achieves variable selection and coefficients shrinkage in a data based manner. It constructively deals with the explanatory variables that tend to be highly collinear by encouraging grouping effect. In addition, it allows for different degree of shrinkages for different coefficients. Re...

BACKGROUND LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (ii...

Automated scoring systems used for the evaluation of spoken or written responses in language assessments need to balance good empirical performance with the interpretability of the scoring models. We compare several methods of feature selection for such scoring systems and show that the use of shrinkage methods such as Lasso regression makes it possible to rapidly build models that both satisfy...

Recently, variational Bayesian (VB) techniques have been applied to probabilistic matrix factorization and shown to perform very well in experiments. In this paper, we theoretically elucidate properties of the VB matrix factorization (VBMF) method. Through finite-sample analysis of the VBMF estimator, we show that two types of shrinkage factors exist in the VBMF estimator: the positive-part Jam...

Presented at the RSS annual meeting 2010, Brighton, U.K. The work discussed here represents collaborations with many people, especially Bradley Efron, Jerome Friedman, Trevor Hastie, Holger Hoefling, Iain Johnstone, Ryan Tibshirani and Daniela Witten I would like to thank the research section of the Royal Statistical Society for inviting me to present this retrospective paper. In this paper I g...

Note that νj([2S−1]) = 1 and νj(ρ) is a strictly increasing function for ρ < [S−1]−1. The proof of Theorem 2 in Wainwright (2009) establishes that for each value of the tuning parameter λ the necessary condition for the signed support recovery is |νj + Z̃j| ≤ 1 with Z̃j = λ−1XT j ΠX⊥ K ( ). Note that Zj follows a non-degenerate zeromean gaussian distribution for S < n, thus the probability of the...

For general estimable parameters in a nonparametric setup, shrinkage (Stein-rule) and preliminary test estimator versions of U-statistics are considered for the (multi-parameter) minimum risk sequential estimation problem. In the usual fashion, allowing the cost per unit sample to be small, an asymptotic model is framed, and in this setup, the asymptotic distributional risks of these versions o...