A Varying Coefficients Model For Estimating Finite Population Totals: A Hierarchical Bayesian Approach

Ciro Velasco-Cruz (), Luis Fernando Contreras-Cruz, Eric P. Smith and José E. Rodríguez
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Ciro Velasco-Cruz: Colegio de Postgraduados
Luis Fernando Contreras-Cruz: Universidad Autónoma Chapingo
Eric P. Smith: Virginia Tech
José E. Rodríguez: Universidad de Guanajuato

Journal of Agricultural, Biological and Environmental Statistics, 2016, vol. 21, issue 3, No 9, 548-568

Abstract: Abstract In some finite sampling situations, there is a primary variable that is sampled, and there are measurements on covariates for the entire population. A Bayesian hierarchical model for estimating totals for finite populations is proposed. A nonparametric linear model is assumed to explain the relationship between the dependent variable of interest and covariates. The regression coefficients in the linear model are allowed to vary as a function of a subset of covariates nonparametrically based on B-splines. The generality of this approach makes it robust and applicable to data collected using a variety of sampling techniques, provided the sample is representative of the finite population. A simulation study is carried out to evaluate the performance of the proposed model for the estimation of the population total. Results indicate accurate estimation of population totals using the approach. The modeling approach is used to estimate the total production of avocado for a large group of groves in Mexico.

Keywords: Bayesian hierarchical model; Population total; Varying coefficient model; Auxiliary information; Nonparametric regression model (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s13253-016-0250-9

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