The Annals of Statistics

Nonconcave penalized composite conditional likelihood estimation of sparse Ising models

Lingzhou Xue, Hui Zou, and Tianxi Cai

Full-text: Open access

Abstract

The Ising model is a useful tool for studying complex interactions within a system. The estimation of such a model, however, is rather challenging, especially in the presence of high-dimensional parameters. In this work, we propose efficient procedures for learning a sparse Ising model based on a penalized composite conditional likelihood with nonconcave penalties. Nonconcave penalized likelihood estimation has received a lot of attention in recent years. However, such an approach is computationally prohibitive under high-dimensional Ising models. To overcome such difficulties, we extend the methodology and theory of nonconcave penalized likelihood to penalized composite conditional likelihood estimation. The proposed method can be efficiently implemented by taking advantage of coordinate-ascent and minorization–maximization principles. Asymptotic oracle properties of the proposed method are established with NP-dimensionality. Optimality of the computed local solution is discussed. We demonstrate its finite sample performance via simulation studies and further illustrate our proposal by studying the Human Immunodeficiency Virus type 1 protease structure based on data from the Stanford HIV drug resistance database. Our statistical learning results match the known biological findings very well, although no prior biological information is used in the data analysis procedure.

Article information

Source
Ann. Statist., Volume 40, Number 3 (2012), 1403-1429.

Dates
First available in Project Euclid: 10 August 2012

Permanent link to this document
https://projecteuclid.org/euclid.aos/1344610588

Digital Object Identifier
doi:10.1214/12-AOS1017

Mathematical Reviews number (MathSciNet)
MR3015030

Zentralblatt MATH identifier
1284.62451

Subjects
Primary: 62G20: Asymptotic properties 62P10: Applications to biology and medical sciences
Secondary: 90-08: Computational methods

Keywords
Composite likelihood coordinatewise optimization Ising model minorization–maximization principle NP-dimension asymptotic theory HIV drug resistance database

Citation

Xue, Lingzhou; Zou, Hui; Cai, Tianxi. Nonconcave penalized composite conditional likelihood estimation of sparse Ising models. Ann. Statist. 40 (2012), no. 3, 1403--1429. doi:10.1214/12-AOS1017. https://projecteuclid.org/euclid.aos/1344610588


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Supplemental materials

  • Supplementary material: Supplementary materials for “Non-concave penalized composite likelihood estimation of sparse Ising models”. In this supplementary file, we provide a complete theoretical analysis of the LASSO-penalized composite likelihood estimator for sparse Ising models.