PMID: 9636138Jun 24, 1998Paper

A stochastic approximation algorithm with Markov chain Monte-carlo method for incomplete data estimation problems

Proceedings of the National Academy of Sciences of the United States of America
M G Gu, F H Kong

Abstract

We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation with the censored or truncated regression model, the nonlinear structural measurement error model, and the random effects model. The procedure is based on the general principle of stochastic approximation and the Markov chain Monte-Carlo method. Applying the theory on adaptive algorithms, we derive conditions under which the proposed procedure converges. Simulation studies also indicate that the proposed procedure consistently converges to the maximum likelihood estimate for the structural measurement error logistic regression model.

Citations

Nov 12, 2005·Journal of Pharmacokinetics and Pharmacodynamics·Goonaseelan Colin PillaiJean-Louis Steimer
Dec 6, 2005·Lifetime Data Analysis·Ming Gao GuGuoxin Zuo
Jun 18, 2011·Bio Systems·Sooyoung Cheon, Faming Liang
Sep 21, 2006·Biometrics·William C L Stewart, Elizabeth A Thompson
Apr 5, 2011·Biometrics·Pang DuYuedong Wang
May 13, 2009·Biometrics·Joseph Sexton, Petter Laake
Jan 12, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yizhou Wang, Song-Chun Zhu
Dec 5, 2018·The British Journal of Mathematical and Statistical Psychology·Siliang ZhangYang Liu
Jul 4, 2019·Statistics in Medicine·Gregory Camilli, Eugene Geis
Jun 1, 2010·The Annals of Applied Statistics·Tom A B SnijdersMichael Schweinberger

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