Results 11  20
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6,166
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
, 2001
"... Variable selection is fundamental to highdimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized ..."
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Cited by 948 (62 self)
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Variable selection is fundamental to highdimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized
Reasoning the fast and frugal way: Models of bounded rationality.
 Psychological Review,
, 1996
"... Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisncing, the authors have ..."
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Cited by 611 (30 self)
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between the satisncing "Take The Best" algorithm and various "rational" inference procedures (e.g., multiple regression). The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms
A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments
 Journal of Business & Economic Statistics
, 2002
"... Weak instruments arise when the instruments in linear instrumental variables (IV) regression are weakly correlated with the included endogenous variables. In generalized method of moments (GMM), more generally, weak instruments correspond to weak identification of some or all of the unknown paramete ..."
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Cited by 484 (11 self)
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Weak instruments arise when the instruments in linear instrumental variables (IV) regression are weakly correlated with the included endogenous variables. In generalized method of moments (GMM), more generally, weak instruments correspond to weak identification of some or all of the unknown
Econometric methods for fractional response variables with an application to 401 (K) plan participation rates
, 1996
"... We develop attractive functional forms and simple quasilikelihood estimation methods for regression models with a fractional dependent variable. Compared with logodds type procedures, there is no difficulty in recovering the regression function for the fractional variable, and there is no need to ..."
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Cited by 472 (8 self)
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We develop attractive functional forms and simple quasilikelihood estimation methods for regression models with a fractional dependent variable. Compared with logodds type procedures, there is no difficulty in recovering the regression function for the fractional variable, and there is no need
MML ESTIMATORS AND ROBUST CLASSIFICATION AND LINEAR REGRESSION PROCEDURES By
"... TITLE: 11ML estimators and robust classification and linear regression procedures ..."
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TITLE: 11ML estimators and robust classification and linear regression procedures
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 476 (46 self)
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of equations in the dual space. While the SVM classifier has a large margin interpretation, the LSSVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization
On the First Order Regression Procedure of Estimation for Incomplete Regression Models
, 1999
"... This article discusses some properties of the first order regression method for imputation of missing values on an explanatory variable in linear regression model and presents an estimation strategy based on hypothesis testing. 1 Introduction When some observations on some of the explanatory variab ..."
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variables in a linear regression model are missing, there are several imputation procedures to obtain their substitutes; see, e.g., Little and Rubin (1987) and Rao and Toutenburg (1995) for an interesting account. Among them, a popular procedure is the method of first order regression. It essentially
Consistency of cross validation for comparing regression procedures. Annals of Statistics, Accepted paper
"... Theoretical developments on cross validation (CV) have mainly focused on selecting one among a list of finitedimensional models (e.g., subset or order selection in linear regression) or selecting a smoothing parameter (e.g., bandwidth for kernel smoothing). However, little is known about consistenc ..."
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Cited by 27 (4 self)
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Theoretical developments on cross validation (CV) have mainly focused on selecting one among a list of finitedimensional models (e.g., subset or order selection in linear regression) or selecting a smoothing parameter (e.g., bandwidth for kernel smoothing). However, little is known about
A Comparison of Quadratic Versus Segmented Regression Procedures for Estimating Nutrient Requirements
 Poult. Sci
, 2002
"... ABSTRACT Continued improvements in dietary formulation will require increasingly detailed knowledge of nutrient requirements. The objective of this study was to evaluate bias and precision of estimates of nutrient requirements from quadratic versus segmented regression. One hundred 0to3wk turke ..."
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Cited by 6 (4 self)
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ABSTRACT Continued improvements in dietary formulation will require increasingly detailed knowledge of nutrient requirements. The objective of this study was to evaluate bias and precision of estimates of nutrient requirements from quadratic versus segmented regression. One hundred 0to3wk
A BootstrapRegression Procedure to Capture Unit Specific Effects in Data Envelopment
, 2004
"... The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes th ..."
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Cited by 1 (0 self)
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The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes
Results 11  20
of
6,166