Latent factor models for density estimation
Publication
, Journal Article
Kundu, S; Dunson, DB
Published in: Biometrika
January 1, 2014
Although discrete mixture modelling has formed the backbone of the literature on Bayesian density estimation, there are some well-known disadvantages. As an alternative to discrete mixtures, we propose a class of priors based on random nonlinear functions of a uniform latent variable with an additive residual. The induced prior for the density is shown to have desirable properties, including ease of centring on an initial guess, large support, posterior consistency and straightforward computation via Gibbs sampling. Some advantages over discrete mixtures, such as Dirichlet process mixtures of Gaussian kernels, are discussed and illustrated via simulations and an application. © 2014 Biometrika Trust.
Duke Scholars
Published In
Biometrika
DOI
EISSN
1464-3510
ISSN
0006-3444
Publication Date
January 1, 2014
Volume
101
Issue
3
Start / End Page
641 / 654
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 3802 Econometrics
- 1403 Econometrics
- 0104 Statistics
- 0103 Numerical and Computational Mathematics
Citation
APA
Chicago
ICMJE
MLA
NLM
Kundu, S., & Dunson, D. B. (2014). Latent factor models for density estimation. Biometrika, 101(3), 641–654. https://doi.org/10.1093/biomet/asu019
Kundu, S., and D. B. Dunson. “Latent factor models for density estimation.” Biometrika 101, no. 3 (January 1, 2014): 641–54. https://doi.org/10.1093/biomet/asu019.
Kundu S, Dunson DB. Latent factor models for density estimation. Biometrika. 2014 Jan 1;101(3):641–54.
Kundu, S., and D. B. Dunson. “Latent factor models for density estimation.” Biometrika, vol. 101, no. 3, Jan. 2014, pp. 641–54. Scopus, doi:10.1093/biomet/asu019.
Kundu S, Dunson DB. Latent factor models for density estimation. Biometrika. 2014 Jan 1;101(3):641–654.
Published In
Biometrika
DOI
EISSN
1464-3510
ISSN
0006-3444
Publication Date
January 1, 2014
Volume
101
Issue
3
Start / End Page
641 / 654
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 3802 Econometrics
- 1403 Econometrics
- 0104 Statistics
- 0103 Numerical and Computational Mathematics