Statistical and Computational Tradeoffs in Stochastic Composite Likelihood
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:129-136, 2009.
Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic variation of the composite likelihood function. We prove the consistency of the estimators, provide formulas for their asymptotic variance and computational complexity, and discuss experimental results in the context of Boltzmann machines and conditional random fields. The theoretical and experimental studies demonstrate the effectiveness of the estimators in achieving a predefined balance between computational complexity and statistical accuracy.