Journal ArticleDOI
A kernel method of estimating structured nonparametric regression based on marginal integration
Oliver Linton,Jens Perch Nielsen +1 more
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In this paper, a simple kernel procedure based on marginal integration that estimates the relevant univariate quantity in both additive and multiplicative nonparametric regression is defined, which is used as a preliminary diagnostic tool.Abstract:
SUMMARY We define a simple kernel procedure based on marginal integration that estimates the relevant univariate quantity in both additive and multiplicative nonparametric regression Nonparametric regression is frequently used as a preliminary diagnostic tool It is a convenient method of summarising the relationship between a dependent and a univariate independent variable However, when the explanatory variables are multidimensional, these methods are less satisfactory In particular, the rate of convergence of standard estimators is poorer, while simple plots are not available to aid model selection There are a number of simplifying structures that have been used to avoid these problems These include the regression tree structure of Gordon & Olshen (1980), the projection pursuit model of Friedman & Stuetzle (1981), semiparametric models such as consideredread more
Citations
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Journal ArticleDOI
Matching As An Econometric Evaluation Estimator
TL;DR: In this article, a rigorous distribution theory for kernel-based matching is presented, and the method of matching is extended to more general conditions than the ones assumed in the statistical literature on the topic.
Book
A Distribution-Free Theory of Nonparametric Regression
TL;DR: How to Construct Nonparametric Regression Estimates * Lower Bounds * Partitioning Estimates * Kernel Estimates * k-NN Estimates * Splitting the Sample * Cross Validation * Uniform Laws of Large Numbers
Book
Nonparametric and Semiparametric Models
TL;DR: In this paper, the authors proposed a nonparametric density estimator based on Histogram and Nonparametric Density Estimation (NDE), and generalized additive models and generalized partial linear models.
Book ChapterDOI
Local Regression Models
TL;DR: Local regression models as discussed by the authors are regression models where the parameters are localized, that is, they are allowed to vary with some or all of the covariates in a general way.
Report SeriesDOI
Endogeneity in nonparametric and semiparametric regression models
Richard Blundell,James L. Powell +1 more
TL;DR: In this article, the authors consider the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors and identify the "average structural function" as a parameter of central interest.
References
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Book
Applied Nonparametric Regression
TL;DR: This chapter discusses smoothing in high Dimensions, Investigating multiple regression by additive models, and incorporating parametric components and alternatives.
Journal ArticleDOI
Root-n-consistent semiparametric regression
TL;DR: In this article, a variable aleatoire (X,Z) dans #7B-R P ×#7b-R q is considered, and an estimateur generalisant l'estimateur des moindres carres ordinaires en inserant des estimateurs non parametriques de la regression dans la projection orthogonale non lineaire sur Z is constructed.
Journal ArticleDOI
Projection Pursuit Regression
TL;DR: In this article, a nonparametric multiple regression (NMM) method is presented, which models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner.
Journal ArticleDOI
Applied Nonparametric Regression.
Peter M. Robinson,W. Haerdle +1 more
TL;DR: Applied Nonparametric Regression as mentioned in this paper is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable, including kernel smoothing, spline smoothing and orthogonal polynomials.