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A stochastic approximation algorithm with Markov chain Monte-carlo method for incomplete data estimation problems.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gu, Mingzhe Kong, F. H. |
| Copyright Year | 1998 |
| 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. |
| File Format | PDF HTM / HTML |
| DOI | 10.1073/pnas.95.13.7270 |
| PubMed reference number | 9636138 |
| Journal | Medline |
| Volume Number | 95 |
| Issue Number | 13 |
| Alternate Webpage(s) | http://www.pnas.org/content/95/13/7270.full.pdf?origin=publication_detail |
| Alternate Webpage(s) | http://www.sta.cuhk.edu.hk/minggao/Public/gstoch3.ps |
| Alternate Webpage(s) | https://doi.org/10.1073/pnas.95.13.7270 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
