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Nonparametric Estimation of Regression Functions
 in the Presence of Irrelevant Regressors.” The Review of Economics and Statistics
, 2007
"... In this paper we propose a method for nonparametric regression which admits continuous and categorical data in a natural manner using the method of kernels. A datadriven method of bandwidth selection is proposed, and we establish the asymptotic normality of the estimator. We also establish the rate ..."
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Cited by 175 (17 self)
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In this paper we propose a method for nonparametric regression which admits continuous and categorical data in a natural manner using the method of kernels. A datadriven method of bandwidth selection is proposed, and we establish the asymptotic normality of the estimator. We also establish
Multivariate adaptive regression splines
 The Annals of Statistics
, 1991
"... A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automaticall ..."
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Cited by 700 (2 self)
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A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations
Projection Pursuit Regression
 Journal of the American Statistical Association
, 1981
"... A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, ..."
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Cited by 550 (6 self)
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A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures
A tutorial on support vector regression
, 2004
"... In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing ..."
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Cited by 865 (3 self)
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In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing
Regression Shrinkage and Selection Via the Lasso
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1994
"... We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactl ..."
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Cited by 4212 (49 self)
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an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and treebased models are briefly described.
Greedy Function Approximation: A Gradient Boosting Machine
 Annals of Statistics
, 2000
"... Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additi ..."
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Cited by 1000 (13 self)
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for additive expansions based on any tting criterion. Specic algorithms are presented for least{squares, least{absolute{deviation, and Huber{M loss functions for regression, and multi{class logistic likelihood for classication. Special enhancements are derived for the particular case where the individual
Estimation of jump regression functions
 Bull. Info. Cybernet
, 1991
"... Qui [1] discussed the estimation problem of jump regression functions which were divided into eight types. L2consistent estimates of two types of them were obtained. This paper studies further this topics and obtains L2 consistent estimates of the other four types. For the last two types, the author ..."
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Cited by 10 (5 self)
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Qui [1] discussed the estimation problem of jump regression functions which were divided into eight types. L2consistent estimates of two types of them were obtained. This paper studies further this topics and obtains L2 consistent estimates of the other four types. For the last two types
Nonparametric estimation of average treatment effects under exogeneity: a review
 REVIEW OF ECONOMICS AND STATISTICS
, 2004
"... Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogen ..."
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Cited by 630 (25 self)
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considered estimation and inference for average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functionalform assumptions. Various methods of semiparametric estimation have been proposed, including estimating the unknown regression functions
Estimation of smooth regression functions in
"... We consider the estimation of smooth regression functions in a class of conditionally parametric covariateresponse models. Independent and identically distributed observations are available from the distribution of (Z,X), where Z is a real–valued covariate with some unknown distribution, and the r ..."
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We consider the estimation of smooth regression functions in a class of conditionally parametric covariateresponse models. Independent and identically distributed observations are available from the distribution of (Z,X), where Z is a real–valued covariate with some unknown distribution
Results 1  10
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15,593