Work

A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations

Year: 2015

Type: article

Abstract: We propose a two-stage penalized least squares method to build large systems of structural equations based on the instrumental variables view of the classical two-stage least squares method. We show that, with large numbers of endogenous and exogenous variables, the system can be constructed via consistent estimation of a set of conditional expectations at the first stage, and consistent selection of regulatory effects at the second stage. While the consistent estimation at the first stage can be obtained via the ridge regression, the adaptive lasso is employed at the second stage to achieve the consistent selection. This method is computationally fast and allows for parallel implementation. We demonstrate its effectiveness via simulation studies and real data analysis.

Source: PubMed

DOI: 10.48550/arxiv.1511.00370

Authors: Chen, Chen, Ren, Min, Zhang, Min, Zhang, Dabao

Institution: Purdue University West Lafayette

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Purdue University, National Institutes of Health, National Cancer Institute

Awards: CAREER: A New Regularization Framework for Identifying Composite Signatures, G6671297155, New Statistical Methods to Model Metabolite Profiles for Disease Detection