We discuss two methods of making nonparametric Bayesian inference on probability measures subject to a partial stochastic ordering. The first method involves a nonparametric prior for a measure on partially ordered latent observations, the second involves rejection sampling. Computational approaches are discussed for each method, and interpretations of prior and posterior information are discus...

This paper concerns semiparametric regression models with additive nonparametric components and high dimensional parametric components under sparsity assumptions. To achieve simultaneous model selection for both nonparametric and parametric parts, we introduce a penalty that combines the adaptive empirical L2-norms of the nonparametric component functions and the SCAD penalty on the coefficient...

We develop a model and a numerical estimation scheme for a Bayesian approach to inference in case-control studies with errors in covariables. The model proposed in this paper is based on a nonparametric model for the unknown joint distribution for the missing data, the observed covariates and the proxy. This nonparametric distribution deenes the measurement error component of the model which re...

in this paper various arch models and relevant news impact curves including a partially nonparametric (pnp) one are compared and estimated with daily iran stock return data. diagnostic tests imply the asymmetry of the volatility response to news. the egarch model, which passes all the tests and appears relatively matching with the asymmetry in the data, seems to be the most adequate characteriz...

Naturally ventilated tropical greenhouse is classified as a complex system because it involves with nonlinear process and multivariable system. The purpose of this study is to determine the mathematical model of NVTG climates to in order to describe and predict the dynamic behavior of temperature and humidity inside NVTG for development of its control system. The modeling of the system is divid...