Local polynomial regression for circular predictors

Local Polynomial Regression

Literature Review for Local Polynomial Regression

This paper discusses key results from the literature in the field of local polynomial regression. Local polynomial regression (LPR) is a nonparametric technique for smoothing scatter plots and modeling functions. For each point, x0, a low-order polynomial WLS regression is fit using only points in some “neighborhood” of x0. The result is a smooth function over the support of the data. LPR has g...

Local Asymptotics for Polynomial Spline Regression

In this paper we develop a general theory of local asymptotics for least squares estimates over polynomial spline spaces in a regression problem. The polynomial spline spaces we consider include univariate splines, tensor product splines, and bivariate or multivariate splines on triangulations. We establish asymptotic normality of the estimate and study the magnitude of the bias due to spline a...

Local Polynomial Regression for Small Area Estimation

Estimation of small area means in the presence of area level auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. The assumptions on the area level regression are considerably weaker than standard small area models. Both the small area mean functions and the between area variance function are modeled as smooth functions of area level covar...

Robust Local Polynomial Regression for Dependent Data

Let (Xj , Yj) n j=1 be a realization of a bivariate jointly strictly stationary process. We consider a robust estimator of the regression function m(x) = E(Y |X = x) by using local polynomial regression techniques. The estimator is a local M-estimator weighted by a kernel function. Under mixing conditions satisfied by many time series models, together with other appropriate conditions, consiste...