title: Hierarchical Bayesian modeling in dichotomous processes in the presence of nonresponse.
abstract: Sampling units that do not answer a survey may dramatically affect the estimation results of interest. The response may even be conditional on the outcome of interest in the survey. If estimates are found using only those who responded, the estimate may be biased, known as nonresponse bias. We are interested in finding estimates of success rates from a survey. We begin by looking at two current Bayesian approaches to treating nonresponse in a hierarchical model. However, these approaches do not consider possible spatial correlations between domains for either success rate or response rate. We build a Bayesian hierarchical spatial model to explicitly estimate the success rate, response rate given success, and response rate given failure. The success rates in the domains of the survey are allowed to be spatially correlated. We also allow spatial dependence between domains in both response rate given success and response rate given failure. Spatial dependence is induced by a common latent spatial structure between the two conditional response rates. We use the 1998 Missouri Turkey Hunting Survey to illustrate this methodology. We find significant spatial correlation in the success rates and incorporating nonrespondents has an impact on the success rate estimates.
authors.affiliation: Department of Mathematics and Statistics, Arizona State University, Tempe, Arizona 85287-1804, USA. Jacob.Oleson@asu.edu
authors.forename: Jacob J
authors.initials: JJ
authors.lastname: Oleson
authors.forename: Chong Z
authors.initials: CZ
authors.lastname: He
mesh_terms: D000818:Animals; D000835:Animals, Wild; D001499:Bayes Theorem; D015982:Bias; D001699:Biometry; D003625:Data Collection; D016014:Linear Models; D008390:Markov Chains; D008923:Missouri; D009010:Monte Carlo Method; D012494:Sampling Studies; D014422:Turkeys