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A platform for research: civil engineering, architecture and urbanism
Estimation of Urinary Arsenic Exposure using Copula-Based Regression: A Case Study of West Bengal
Abstract Copula functions are useful in constructing multivariate distributions using various marginal distributions and non-linear dependence structures. This paper uses copula functions to construct multivariate distributions with one-sided marginal distributions and copula-based regression to estimate the arsenic exposure in urine using arsenic intake through water and diet as potential covariates in a study in West Bengal, India. As arsenic concentration in urine, water, and diet takes only positive values and negative amount of arsenic concentration in urine, water, and diet are impossibility, one-sided distribution with positive real line support is assumed for these characteristics. Gaussian and Farlie-Gumbel-Morgenstern (FGM) families of copula are considered to capture the non-linear association among these variables. The maximum likelihood method is applied to estimate the parameters of the models. The estimation result suggests that the covariates significantly affect the urinary arsenic exposure, and arsenic intake through diet was a potential contributor of urinary arsenic exposure even when arsenic intake through water was reduced significantly in this arsenic-affected region of West Bengal.
Estimation of Urinary Arsenic Exposure using Copula-Based Regression: A Case Study of West Bengal
Abstract Copula functions are useful in constructing multivariate distributions using various marginal distributions and non-linear dependence structures. This paper uses copula functions to construct multivariate distributions with one-sided marginal distributions and copula-based regression to estimate the arsenic exposure in urine using arsenic intake through water and diet as potential covariates in a study in West Bengal, India. As arsenic concentration in urine, water, and diet takes only positive values and negative amount of arsenic concentration in urine, water, and diet are impossibility, one-sided distribution with positive real line support is assumed for these characteristics. Gaussian and Farlie-Gumbel-Morgenstern (FGM) families of copula are considered to capture the non-linear association among these variables. The maximum likelihood method is applied to estimate the parameters of the models. The estimation result suggests that the covariates significantly affect the urinary arsenic exposure, and arsenic intake through diet was a potential contributor of urinary arsenic exposure even when arsenic intake through water was reduced significantly in this arsenic-affected region of West Bengal.
Estimation of Urinary Arsenic Exposure using Copula-Based Regression: A Case Study of West Bengal
Das, Arabinda (author)
2014
Article (Journal)
Electronic Resource
English
BKL:
43.00
Umweltforschung, Umweltschutz: Allgemeines
/
43.00$jUmweltforschung$jUmweltschutz: Allgemeines
Estimation of Urinary Arsenic Exposure using Copula-Based Regression: A Case Study of West Bengal
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