Processing math: 100%



Home Page

Papers

Submissions

News

Editorial Board

Special Issues

Open Source Software

Proceedings (PMLR)

Data (DMLR)

Transactions (TMLR)

Search

Statistics

Login

Frequently Asked Questions

Contact Us



RSS Feed

High-Dimensional Poisson Structural Equation Model Learning via 1-Regularized Regression

Gunwoong Park, Sion Park; 20(95):1−41, 2019.

Abstract

In this paper, we develop a new approach to learning high-dimensional Poisson structural equation models from only observational data without strong assumptions such as faithfulness and a sparse moralized graph. A key component of our method is to decouple the ordering estimation or parent search where the problems can be efficiently addressed using 1-regularized regression and the moments relation. We show that sample size n=Ω(d2log9p) is sufficient for our polynomial time Moments Ratio Scoring (MRS) algorithm to recover the true directed graph, where p is the number of nodes and d is the maximum indegree. We verify through simulations that our algorithm is statistically consistent in the high-dimensional p>n setting, and performs well compared to state-of-the-art ODS, GES, and MMHC algorithms. We also demonstrate through multivariate real count data that our MRS algorithm is well-suited to estimating DAG models for multivariate count data in comparison to other methods used for discrete data.

[abs][pdf][bib]       
© JMLR 2019. (edit, beta)

Mastodon