Maximum‐likelihood estimation for constrained‐ or missing‐data models

DOI10.2307/3315756zbMath0785.62058OpenAlexW1994872300MaRDI QIDQ4275250

Alan E. Gelfand, Bradley P. Carlin

Publication date: 26 April 1994

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3315756

zbMATH Keywords

EM algorithmnumerical examplesGibbs samplermaximum-likelihood estimatesmissing dataapproximate likelihoodconstrained dataratio of integralsMonte Carlo approximants

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Probabilistic methods, stochastic differential equations (65C99)

Related Items (10)

Fixed versus mixed effects based marginal models for clustered correlated binary data: an overview on advances and challenges ⋮ Robust and consistent estimation of generators in credit risk ⋮ Monte Carlo likelihood inference for missing data models ⋮ Likelihood Inference for Gibbs Processes in the Analysis of Spatial Point Patterns ⋮ State estimation for aoristic models ⋮ The S-U algorithm for missing data problems ⋮ Efficient simulated maximum likelihood estimation through explicitly parameter dependent importance sampling ⋮ Asymptotics of maximum likelihood estimators based on Markov chain Monte Carlo methods ⋮ Parameter estimation for hidden Markov chains ⋮ A stochastic approximation algorithm for maximum-likelihood estimation with incomplete data

Cites Work

• Data. A collection of problems from many fields for the student and research worker
• Markov chains for exploring posterior distributions. (With discussion)
• Simulation and the Asymptotics of Optimization Estimators
• Sampling-Based Approaches to Calculating Marginal Densities
• A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration
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