Electronic Journal of Statistics

Estimation of high-dimensional partially-observed discrete Markov random fields

Yves F. Atchade

Full-text: Open access

Abstract

We consider the problem of estimating the parameters of discrete Markov random fields from partially observed data in a high-dimensional setting. Using a $\ell^{1}$-penalized pseudo-likelihood approach, we fit a misspecified model obtained by ignoring the missing data problem. We derive an estimation error bound that highlights the effect of the misspecification. We report some simulation results that illustrate the theoretical findings.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 2242-2263.

Dates
First available in Project Euclid: 31 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1414761922

Digital Object Identifier
doi:10.1214/14-EJS946

Mathematical Reviews number (MathSciNet)
MR3273625

Zentralblatt MATH identifier
1302.62206

Subjects
Primary: 62M40: Random fields; image analysis 62G20: Asymptotic properties

Keywords
Network estimation high-dimensional inference penalized likelihood inference misspecification pseudo-likelihood Markov random fields

Citation

Atchade, Yves F. Estimation of high-dimensional partially-observed discrete Markov random fields. Electron. J. Statist. 8 (2014), no. 2, 2242--2263. doi:10.1214/14-EJS946. https://projecteuclid.org/euclid.ejs/1414761922


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