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

Publication ,  Journal Article
Gelfand, AE; Carlin, BP
Published in: Canadian Journal of Statistics
January 1, 1993

In statistical models involving constrained or missing data, likelihoods containing integrals emerge. In the case of both constrained and missing data, the result is a ratio of integrals, which for multivariate data may defy exact or approximate analytic expression. Seeking maximum‐likelihood estimates in such settings, we propose Monte Carlo approximants for these integrals, and subsequently maximize the resulting approximate likelihood. Iteration of this strategy expedites the maximization, while the Gibbs sampler is useful for the required Monte Carlo generation. As a result, we handle a class of models broader than the customary EM setting without using an EM‐type algorithm. Implementation of the methodology is illustrated in two numerical examples. Copyright © 1993 Statistical Society of Canada

Duke Scholars

Published In

Canadian Journal of Statistics

DOI

EISSN

1708-945X

ISSN

0319-5724

Publication Date

January 1, 1993

Volume

21

Issue

3

Start / End Page

303 / 311

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

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Gelfand, A. E., & Carlin, B. P. (1993). Maximum‐likelihood estimation for constrained‐ or missing‐data models. Canadian Journal of Statistics, 21(3), 303–311. https://doi.org/10.2307/3315756
Gelfand, A. E., and B. P. Carlin. “Maximum‐likelihood estimation for constrained‐ or missing‐data models.” Canadian Journal of Statistics 21, no. 3 (January 1, 1993): 303–11. https://doi.org/10.2307/3315756.
Gelfand AE, Carlin BP. Maximum‐likelihood estimation for constrained‐ or missing‐data models. Canadian Journal of Statistics. 1993 Jan 1;21(3):303–11.
Gelfand, A. E., and B. P. Carlin. “Maximum‐likelihood estimation for constrained‐ or missing‐data models.” Canadian Journal of Statistics, vol. 21, no. 3, Jan. 1993, pp. 303–11. Scopus, doi:10.2307/3315756.
Gelfand AE, Carlin BP. Maximum‐likelihood estimation for constrained‐ or missing‐data models. Canadian Journal of Statistics. 1993 Jan 1;21(3):303–311.
Journal cover image

Published In

Canadian Journal of Statistics

DOI

EISSN

1708-945X

ISSN

0319-5724

Publication Date

January 1, 1993

Volume

21

Issue

3

Start / End Page

303 / 311

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 1403 Econometrics
  • 0104 Statistics