We consider the estimation of high-dimensional network structures from
partially observed Markov random field data using a penalized pseudo-likelihood
approach. We fit a misspecified model obtained by ignoring the missing data
problem. We study the consistency of the estimator and derive a bound on its
rate of convergence. The results obtained relate the rate of convergence of the
estimator to the extent of the missing data problem. We report some simulation
results that empirically validate some of the theoretical findings.Comment: 24 pages 1 figur