Nonparametric Bayesian modeling for monotonicity in catch ratio

Abstract

ABSTRACTThis article proposes nonparametric Bayesian approaches to monotone function estimation. This approach uses a hierarchical Bayes framework and a characterization of stick-breaking process that allows unconstrained estimation of the monotone function. In order to avoid the limitation of parametric modeling, a general class of prior distributions, called stick-breaking priors, is considered. It accommodates much more flexible forms and can easily deal with skewness, multimodality, etc., of the dependent variable response. The proposed approach is incorporated to model the catch ratio based on automatic weather station (AWS) data.