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Bayesian Copula Density Deconvolution for Zero-Inflated Data in Nutritional Epidemiology.

Authors :
Sarkar, Abhra
Pati, Debdeep
Mallick, Bani K.
Carroll, Raymond J.
Source :
Journal of the American Statistical Association. Sep2021, Vol. 116 Issue 535, p1075-1087. 13p.
Publication Year :
2021

Abstract

Estimating the marginal and joint densities of the long-term average intakes of different dietary components is an important problem in nutritional epidemiology. Since these variables cannot be directly measured, data are usually collected in the form of 24-hr recalls of the intakes, which show marked patterns of conditional heteroscedasticity. Significantly compounding the challenges, the recalls for episodically consumed dietary components also include exact zeros. The problem of estimating the density of the latent long-time intakes from their observed measurement error contaminated proxies is then a problem of deconvolution of densities with zero-inflated data. We propose a Bayesian semiparametric solution to the problem, building on a novel hierarchical latent variable framework that translates the problem to one involving continuous surrogates only. Crucial to accommodating important aspects of the problem, we then design a copula based approach to model the involved joint distributions, adopting different modeling strategies for the marginals of the different dietary components. We design efficient Markov chain Monte Carlo algorithms for posterior inference and illustrate the efficacy of the proposed method through simulation experiments. Applied to our motivating nutritional epidemiology problems, compared to other approaches, our method provides more realistic estimates of the consumption patterns of episodically consumed dietary components. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01621459
Volume :
116
Issue :
535
Database :
Academic Search Index
Journal :
Journal of the American Statistical Association
Publication Type :
Academic Journal
Accession number :
152353676
Full Text :
https://doi.org/10.1080/01621459.2020.1782220