Nonparametric Estimation using Regression Quantiles in a Regression Model

Nonparametric estimation of conditional quantiles using quantile regression trees

A nonparametric regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning of the covariate space is investigated. Unlike least squares regression trees, which concentrate on modeling the relationship between the response and the covariates at the center of the response distribution, our quantile...

@bullet Regression Quantiles in Nonparahetric Regression @bullet Regression Quantiles in Nonparametric Regression

In a nonparametric setup involving stochastic regressors. regression quantiles relate to the so called conditional quantile functions. Various asymptotic properties of such conditional quantile processes are studied with due emphasis on the underlying design aspects.

Nonparametric Regression Estimation under Kernel Polynomial Model for Unstructured Data

The nonparametric estimation(NE) of kernel polynomial regression (KPR) model is a powerful tool to visually depict the effect of covariates on response variable, when there exist unstructured and heterogeneous data. In this paper we introduce KPR model that is the mixture of nonparametric regression models with bootstrap algorithm, which is considered in a heterogeneous and unstructured framewo...

A MODIFICATION ON RIDGE ESTIMATION FOR FUZZY NONPARAMETRIC REGRESSION

This paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. This estimation method is obtained by implementing ridge regression learning algorithm in the La- grangian dual space. The distance measure for fuzzy numbers that suggested by Diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting t...

Nonparametric estimation of a switching regression model