Sciweavers

Share
ICML
2008
IEEE

An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators

9 years 10 months ago
An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators
Statistical and computational concerns have motivated parameter estimators based on various forms of likelihood, e.g., joint, conditional, and pseudolikelihood. In this paper, we present a unified framework for studying these estimators, which allows us to compare their relative (statistical) efficiencies. Our asymptotic analysis suggests that modeling more of the data tends to reduce variance, but at the cost of being more sensitive to model misspecification. We present experiments validating our analysis.
Percy Liang, Michael I. Jordan
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2008
Where ICML
Authors Percy Liang, Michael I. Jordan
Comments (0)
books