In a system of regression models, finding feasible shrinkage is demanding since the covariance structure unknown and cannot be ignored. On other hand, specifying sub-space restrictions for adequate vital. This study proposes estimation strategies where restriction obtained from LASSO. Therefore, some LASSO-based Stein-type estimators are introduced, their asymptotic performance studied. Extensi...

Hans C. van Houwelingen Saskia le Cessie Department of Medical Statistics, Leiden, The Netherlands P.O.Box 9604 2300 RC Leiden, The Netherlands email: [email protected] Abstract A review is given of shrinkage and penalization as tools to improve predictive accuracy of regression models. The James-Stein estimator is taken as starting point. Procedures covered are the Pre-test Estimation, ...

Partial linear model is very flexible when the relation between the covariates and responses, either parametric and nonparametric. However, estimation of the regression coefficients is challenging since one must also estimate the nonparametric component simultaneously. As a remedy, the differencing approach, to eliminate the nonparametric component and estimate the regression coefficients, can ...

S 1.2 S. EJAZ AHMED University of Windsor Panelized and Shrinkage Estimation in Partially Linear Models Panelized and shrinkage regression have been widely used in high-dimensional data analysis. Much recent work has been done on the study of penalized least square methods in linear models. In this talk, we consider an absolute penalty type estimator (APE) for partially linear models, which is ...

We study a general algorithm to improve accuracy in cluster analysis that employs the James-Stein shrinkage effect in k-means clustering. We shrink the centroids of clusters toward the overall mean of all data using a James-Stein-type adjustment, and then the James-Stein shrinkage estimators act as the new centroids in the next clustering iteration until convergence. We compare the shrinkage re...

We study the problem of variable selection for linear transformation models, a class of general semiparametric models for censored survival data. The penalized marginal likelihood methods with shrinkage-type penalties are proposed to automate variable selection in linear transformation models; we consider the LASSO penalty and propose a new penalty called the adaptive-LASSO (ALASSO). Unlike the...

In this paper, we consider improved estimation strategies for the parameter vector in multiple regression models with first-order random coefficient autoregressive errors (RCAR(1)). We propose a shrinkage estimation strategy and implement variable selection methods such as lasso and adaptive lasso strategies. The simulation results reveal that the shrinkage estimators perform better than both l...

In a two-stage linear regression model with Normal noise, I consider James–Stein type shrinkage in the estimation of the first-stage instrumental variable coefficients. For at least four instrumental variables and a single endogenous regressor, I show that the standard two-stage least-squares estimator is dominated with respect to bias. I construct the dominating estimator by a variant of James...

In this paper we propose James–Stein type estimators for variances raised to a fixed power by shrinking individual variance estimators towards the arithmetic mean. We derive and estimate the optimal choices of shrinkage parameters under both the squared and the Stein loss functions. Asymptotic properties are investigated under two schemes when either the number of degrees of freedom of each ind...