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A Class of Non-parametric Estimates of a Smooth Regression Function

A Class of Non-parametric Estimates of a Smooth Regression Function
Author: R. M. Royall
Publisher:
Total Pages: 164
Release: 1966
Genre: Mathematical statistics
ISBN:

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The purpose of the paper is to develop methods for estimating regression functions (i.e., conditional expectations) when nothing is known or assumed about their underlying functional form. The approach is 'non-parametric' in the sense that the regression function itself, rather than a set of numerical parameters, is estimated. The methods given make use of certain rank order statistics and thereby avoid problems of scaling which are troublesome when less sophisticated non-parametric methods are used. The large sample performance of the proposed regression estimators is studied in detail and methods for obtaining high orders of asymptotic efficiency are given. The asymptotic (normal) distribution of the estimates is obtained and the related problem of prediction is discussed. (Author).


A Distribution-Free Theory of Nonparametric Regression

A Distribution-Free Theory of Nonparametric Regression
Author: László Györfi
Publisher: Springer Science & Business Media
Total Pages: 662
Release: 2002-08-12
Genre: Mathematics
ISBN: 0387954414

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This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.


Applied Nonparametric Regression

Applied Nonparametric Regression
Author: Wolfgang Härdle
Publisher: Cambridge University Press
Total Pages: 356
Release: 1990
Genre: Business & Economics
ISBN: 9780521429504

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This is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable.


Smooth Nonparametric Regression Analysis

Smooth Nonparametric Regression Analysis
Author: Gordon J. Johnston
Publisher:
Total Pages: 96
Release: 1979
Genre: Mathematical statistics
ISBN:

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This work investigates properties of the Watson type estimator of the regression function EY bar X = x of a bivariate random vector (X, Y). Pointwise weak consistency of the estimator is demonstrated. Sufficient conditions are given for asymptotic joint normality of the estimator taken at a finite number of distinct points, and for the uniform consistency of the estimator over a bounded interval. A large sample confidence band for the regression function, based on the estimator, is derived. (Author).