Results 1  10
of
1,286
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 ..."
Abstract

Cited by 4212 (49 self)
 Add to MetaCart
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
Predictive distribution of regression vector and residual sum of squares for normal multiple regression model
 Communications In Statistics: Theory and Methods
, 2004
"... This paper proposes predictive inference for the multiple regression model with independent normal errors. The distributions of the sample regression vector (SRV) and the residual sum of squares (RSS) for the model are derived by using invariant differentials. Also the predictive distributions of th ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
This paper proposes predictive inference for the multiple regression model with independent normal errors. The distributions of the sample regression vector (SRV) and the residual sum of squares (RSS) for the model are derived by using invariant differentials. Also the predictive distributions
RESIDUE SUM RULES FOR INELASTIC (ANTI)NEUTRINO NUCLEON SCATTERING*
, 1974
"... We discuss the complete set of current algebra sum rules for the Regge residues and asymptotic constant Slimits of the structure functions of inelastic (anti) neutrino nucleon scattering. (Submitted to Phys. Rev.) *Work supported by the U. S. Atomic Energy Commission. subject of sum rules for the s ..."
Abstract
 Add to MetaCart
We discuss the complete set of current algebra sum rules for the Regge residues and asymptotic constant Slimits of the structure functions of inelastic (anti) neutrino nucleon scattering. (Submitted to Phys. Rev.) *Work supported by the U. S. Atomic Energy Commission. subject of sum rules
Computation and Analysis of Multiple Structural Change Models.‖
 Journal of Applied Econometrics
, 2003
"... SUMMARY In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the pro ..."
Abstract

Cited by 448 (6 self)
 Add to MetaCart
of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most leastsquares operations of order O T 2 for any number
Asymptotics for Lassotype estimators
, 2000
"... this paper, we consider the asymptotic behaviour of regression estimators that minimize the residual sum of squares plus a penalty proportional to ..."
Abstract

Cited by 264 (3 self)
 Add to MetaCart
this paper, we consider the asymptotic behaviour of regression estimators that minimize the residual sum of squares plus a penalty proportional to
The twoparameter PoissonDirichlet distribution derived from a stable subordinator.
, 1995
"... The twoparameter PoissonDirichlet distribution, denoted pd(ff; `), is a distribution on the set of decreasing positive sequences with sum 1. The usual PoissonDirichlet distribution with a single parameter `, introduced by Kingman, is pd(0; `). Known properties of pd(0; `), including the Markov ..."
Abstract

Cited by 356 (33 self)
 Add to MetaCart
The twoparameter PoissonDirichlet distribution, denoted pd(ff; `), is a distribution on the set of decreasing positive sequences with sum 1. The usual PoissonDirichlet distribution with a single parameter `, introduced by Kingman, is pd(0; `). Known properties of pd(0; `), including the Markov
Nonlinear spatial normalization using basis functions
 Human Brain Mapping
, 1999
"... Abstract: We describe a comprehensive framework for performing rapid and automatic nonlabelbased nonlinear spatial normalizations. The approach adopted minimizes the residual squared difference between an image and a template of the same modality. In order to reduce the number of parameters to be f ..."
Abstract

Cited by 329 (19 self)
 Add to MetaCart
Abstract: We describe a comprehensive framework for performing rapid and automatic nonlabelbased nonlinear spatial normalizations. The approach adopted minimizes the residual squared difference between an image and a template of the same modality. In order to reduce the number of parameters
On the LASSO and Its Dual
 Journal of Computational and Graphical Statistics
, 1999
"... Proposed by Tibshirani (1996), the LASSO (least absolute shrinkage and selection operator) estimates a vector of regression coe#cients by minimising the residual sum of squares subject to a constraint on the l 1 norm of coe#cient vector. The LASSO estimator typically has one or more zero elements ..."
Abstract

Cited by 209 (2 self)
 Add to MetaCart
Proposed by Tibshirani (1996), the LASSO (least absolute shrinkage and selection operator) estimates a vector of regression coe#cients by minimising the residual sum of squares subject to a constraint on the l 1 norm of coe#cient vector. The LASSO estimator typically has one or more zero
Results 1  10
of
1,286