Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials-Reference-Cited by-同舟云学术

Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials

Published:2022-01-01 Issue:1 Volume:16 Page:
ISSN:1935-7524
Container-title:Electronic Journal of Statistics
language:
Short-container-title:Electron. J. Statist.

Author:

Wehrhahn Claudia¹,Barrientos Andrés F.²,Jara Alejandro³

Affiliation:

1. Department of Statistics, University of California Santa Cruz, Santa Cruz, U.S.A

2. Department of Statistics, Florida State University, Florida, U.S.A

3. MiDaS-Center for the Discovery of Structures in Complex Data and Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile

Publisher

Institute of Mathematical Statistics

Subject

Statistics and Probability,Statistics, Probability and Uncertainty

Reference50 articles.

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2. ATCHISON, J. & SHEN, S. M. (1980). Logistic-normal distributions: Some properties and uses. Biometrika 67 261–272.

3. BABU, G. J. & CHAUBEY, Y. P. (2006). Smooth estimation of a distribution and density function on a hypercube using Bernstein polynomials for dependent random vectors. Statistics & Probability Letters 76 959–969.

4. BANERJEE, S., CARLIN, B. P. & GELFAND, A. E. (2003). Hierarchical modeling and analysis for spatial data. Chapman and Hall/CRC.

5. BARNDORFF-NIELSEN, O. (1973). On M-ancillarity. Biometrika 60 447–455.

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