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Fast Moment Estimation for Generalized Latent Dirichlet Models.

Publication ,  Journal Article
Zhao, S; Engelhardt, BE; Mukherjee, S; Dunson, DB
Published in: Journal of the American Statistical Association
January 2018

We develop a generalized method of moments (GMM) approach for fast parameter estimation in a new class of Dirichlet latent variable models with mixed data types. Parameter estimation via GMM has computational and statistical advantages over alternative methods, such as expectation maximization, variational inference, and Markov chain Monte Carlo. A key computational advantage of our method, Moment Estimation for latent Dirichlet models (MELD), is that parameter estimation does not require instantiation of the latent variables. Moreover, performance is agnostic to distributional assumptions of the observations. We derive population moment conditions after marginalizing out the sample-specific Dirichlet latent variables. The moment conditions only depend on component mean parameters. We illustrate the utility of our approach on simulated data, comparing results from MELD to alternative methods, and we show the promise of our approach through the application to several datasets. Supplementary materials for this article are available online.

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Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

January 2018

Volume

113

Issue

524

Start / End Page

1528 / 1540

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

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Zhao, S., Engelhardt, B. E., Mukherjee, S., & Dunson, D. B. (2018). Fast Moment Estimation for Generalized Latent Dirichlet Models. Journal of the American Statistical Association, 113(524), 1528–1540. https://doi.org/10.1080/01621459.2017.1341839
Zhao, Shiwen, Barbara E. Engelhardt, Sayan Mukherjee, and David B. Dunson. “Fast Moment Estimation for Generalized Latent Dirichlet Models.Journal of the American Statistical Association 113, no. 524 (January 2018): 1528–40. https://doi.org/10.1080/01621459.2017.1341839.
Zhao S, Engelhardt BE, Mukherjee S, Dunson DB. Fast Moment Estimation for Generalized Latent Dirichlet Models. Journal of the American Statistical Association. 2018 Jan;113(524):1528–40.
Zhao, Shiwen, et al. “Fast Moment Estimation for Generalized Latent Dirichlet Models.Journal of the American Statistical Association, vol. 113, no. 524, Jan. 2018, pp. 1528–40. Epmc, doi:10.1080/01621459.2017.1341839.
Zhao S, Engelhardt BE, Mukherjee S, Dunson DB. Fast Moment Estimation for Generalized Latent Dirichlet Models. Journal of the American Statistical Association. 2018 Jan;113(524):1528–1540.

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

January 2018

Volume

113

Issue

524

Start / End Page

1528 / 1540

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics