Work

Estimation And Selection Via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

Year: 2011

Type: article

Abstract: . Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results.

Source: PubMed

DOI: 10.48550/arxiv.1112.6363

Authors: Huang, Jian, Zhang, Cun-Hui

Institutions: Rutgers, The State University of New Jersey, University of Iowa

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: National Cancer Institute

Awards: Novel Methods for Integrative Analysis of Cancer Genomic Data, Regularized Classification & Survival Analysis for Expression Profiling of Cancer