Estimation of high-dimensional partially-observed discrete Markov random fields. (English) Zbl 1302.62206

Summary: 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.


62M40 Random fields; image analysis
62G20 Asymptotic properties of nonparametric inference
Full Text: DOI arXiv Euclid


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