Abstract An identification result for nonparametrically transformed location scale models is proven. The constructive in the sense that it provides an explicit expression of transformation function.

In this study, an ARX model with nonparametric multi time scale regression model was proposed for short term gust forecasting and applied to a ski resort in Japan. Proposed ARX model can be used for short term forecasting of mean and fluctuating wind speed. Proposed nonparametric multi time scale regression model can appropriately estimate the peak factor and shows better agreement with the mea...

This paper presents a simple two-step nonparametric estimator for a triangular simultaneous equation model. Our approach employs series approximations that exploit the additive structure of the model. The first step comprises the nonparametric estimation of the reduced form and the corresponding residuals. The second step is the estimation of the primary equation via nonparametric regression wi...

The Dirichlet process is a prior used in nonparametric Bayesian models of data, particularly in Dirichlet process mixture models (also known as infinite mixture models). It is a distribution over distributions, i.e. each draw from a Dirichlet process is itself a distribution. It is called a Dirichlet process because it has Dirichlet distributed finite dimensional marginal distributions, just as...

A Bayesian nonparametric model is a Bayesian model on an infinite-dimensional parameter space. The parameter space is typically chosen as the set of all possible solutions for a given learning problem. For example, in a regression problem the parameter space can be the set of continuous functions, and in a density estimation problem the space can consist of all densities. A Bayesian nonparametr...