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Bayesian modeling of temporal dependence in large sparse contingency tables.

Publication ,  Journal Article
Kunihama, T; Dunson, DB
Published in: Journal of the American Statistical Association
January 2013

In many applications, it is of interest to study trends over time in relationships among categorical variables, such as age group, ethnicity, religious affiliation, political party and preference for particular policies. At each time point, a sample of individuals provide responses to a set of questions, with different individuals sampled at each time. In such settings, there tends to be abundant missing data and the variables being measured may change over time. At each time point, one obtains a large sparse contingency table, with the number of cells often much larger than the number of individuals being surveyed. To borrow information across time in modeling large sparse contingency tables, we propose a Bayesian autoregressive tensor factorization approach. The proposed model relies on a probabilistic Parafac factorization of the joint pmf characterizing the categorical data distribution at each time point, with autocorrelation included across times. Efficient computational methods are developed relying on MCMC. The methods are evaluated through simulation examples and applied to social survey data.

Duke Scholars

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

January 2013

Volume

108

Issue

504

Start / End Page

1324 / 1338

Related Subject Headings

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

Citation

APA
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ICMJE
MLA
NLM
Kunihama, T., & Dunson, D. B. (2013). Bayesian modeling of temporal dependence in large sparse contingency tables. Journal of the American Statistical Association, 108(504), 1324–1338. https://doi.org/10.1080/01621459.2013.823866
Kunihama, Tsuyoshi, and David B. Dunson. “Bayesian modeling of temporal dependence in large sparse contingency tables.Journal of the American Statistical Association 108, no. 504 (January 2013): 1324–38. https://doi.org/10.1080/01621459.2013.823866.
Kunihama T, Dunson DB. Bayesian modeling of temporal dependence in large sparse contingency tables. Journal of the American Statistical Association. 2013 Jan;108(504):1324–38.
Kunihama, Tsuyoshi, and David B. Dunson. “Bayesian modeling of temporal dependence in large sparse contingency tables.Journal of the American Statistical Association, vol. 108, no. 504, Jan. 2013, pp. 1324–38. Epmc, doi:10.1080/01621459.2013.823866.
Kunihama T, Dunson DB. Bayesian modeling of temporal dependence in large sparse contingency tables. Journal of the American Statistical Association. 2013 Jan;108(504):1324–1338.

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

January 2013

Volume

108

Issue

504

Start / End Page

1324 / 1338

Related Subject Headings

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