The Annals of Statistics, ISSN 0090-5364, 4/2010, Volume 38, Issue 2, pp. 894 - 942

We propose MC+, a fast, continuous, nearly unbiased and accurate method of penalized variable selection in high-dimensional linear regression. The LASSO is...

Minimax | Degrees of freedom | Estimation bias | Unbiased estimators | Convexity | Computational complexity | Estimators | Concavity | Vertices | Oracles | Model selection | Nonconvex minimization | Sign consistency | Penalized estimation | Selection consistency | Correct selection | Risk estimation | Unbiasedness | Least squares | Mean squared error | Variable selection | REGRESSION | SPARSITY | penalized estimation | correct selection | DANTZIG SELECTOR | risk estimation | degrees of freedom | model selection | RISK | STATISTICS & PROBABILITY | selection consistency | sign consistency | nonconvex minimization | NONCONCAVE PENALIZED LIKELIHOOD | mean squared error | least squares | unbiasedness | ADAPTIVE LASSO | LARGER | minimax | STATISTICAL ESTIMATION | 62J05 | 62J07 | 62H12 | 62H25

Minimax | Degrees of freedom | Estimation bias | Unbiased estimators | Convexity | Computational complexity | Estimators | Concavity | Vertices | Oracles | Model selection | Nonconvex minimization | Sign consistency | Penalized estimation | Selection consistency | Correct selection | Risk estimation | Unbiasedness | Least squares | Mean squared error | Variable selection | REGRESSION | SPARSITY | penalized estimation | correct selection | DANTZIG SELECTOR | risk estimation | degrees of freedom | model selection | RISK | STATISTICS & PROBABILITY | selection consistency | sign consistency | nonconvex minimization | NONCONCAVE PENALIZED LIKELIHOOD | mean squared error | least squares | unbiasedness | ADAPTIVE LASSO | LARGER | minimax | STATISTICAL ESTIMATION | 62J05 | 62J07 | 62H12 | 62H25

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2008, Volume 36, Issue 4, pp. 1509 - 1533

Fan and Li propose a family of variable selection methods via penalized likelihood using concave penalty functions. The nonconcave penalized likelihood...

Penalty function | Simulations | Linear regression | Lot quality assurance sampling | Regression analysis | Logarithms | Modeling | Estimators | Estimation methods | Oracles | AIC | One-step estimator | Oracle properties | SCAD | BIC | LASSO | REGRESSION | SURROGATE OBJECTIVE FUNCTIONS | STATISTICS & PROBABILITY | FAILURE TIME DATA | VARIABLE SELECTION | oracle properties | one-step estimator | ASYMPTOTICS | REGULARIZATION | 62J05 | 62J07 | Lasso | Oracle Properties

Penalty function | Simulations | Linear regression | Lot quality assurance sampling | Regression analysis | Logarithms | Modeling | Estimators | Estimation methods | Oracles | AIC | One-step estimator | Oracle properties | SCAD | BIC | LASSO | REGRESSION | SURROGATE OBJECTIVE FUNCTIONS | STATISTICS & PROBABILITY | FAILURE TIME DATA | VARIABLE SELECTION | oracle properties | one-step estimator | ASYMPTOTICS | REGULARIZATION | 62J05 | 62J07 | Lasso | Oracle Properties

Journal Article

The Annals of Statistics, ISSN 0090-5364, 6/2008, Volume 36, Issue 3, pp. 1108 - 1126

Coefficient estimation and variable selection in multiple linear regression is routinely done in the (penalized) least squares (LS) framework. The concept of...

Gaussian distributions | Variable coefficients | Least squares | Quantile regression | Modeling | Estimators | Consistent estimators | Oracles | T distribution | Estimation methods | Oracle properties | Model selection | Asymptotic efficiency | Universal lower bound | Linear program | oracle properties | LASSO | model selection | linear program | STATISTICS & PROBABILITY | asymptotic efficiency | universal lower bound | 62J05 | 62J07

Gaussian distributions | Variable coefficients | Least squares | Quantile regression | Modeling | Estimators | Consistent estimators | Oracles | T distribution | Estimation methods | Oracle properties | Model selection | Asymptotic efficiency | Universal lower bound | Linear program | oracle properties | LASSO | model selection | linear program | STATISTICS & PROBABILITY | asymptotic efficiency | universal lower bound | 62J05 | 62J07

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2009, Volume 37, Issue 4, pp. 1733 - 1751

We consider the problem of model selection and estimation in situations where the number of parameters diverges with the sample size. When the dimension is...

Dimensionality | Sample size | High dimensional spaces | Linear regression | Elasticity | Collinearity | Mathematical independent variables | Modeling | Estimators | Oracles | shrinkage methods | elastic-net | NONCONCAVE PENALIZED LIKELIHOOD | Adaptive regularization | SHRINKAGE | high dimensionality | oracle property | LASSO | model selection | STATISTICS & PROBABILITY | MODEL | VARIABLE SELECTION | 62J05 | 62J07 | Elastic-Net | High dimensionality | Shrinkage methods | Model selection | Oracle property

Dimensionality | Sample size | High dimensional spaces | Linear regression | Elasticity | Collinearity | Mathematical independent variables | Modeling | Estimators | Oracles | shrinkage methods | elastic-net | NONCONCAVE PENALIZED LIKELIHOOD | Adaptive regularization | SHRINKAGE | high dimensionality | oracle property | LASSO | model selection | STATISTICS & PROBABILITY | MODEL | VARIABLE SELECTION | 62J05 | 62J07 | Elastic-Net | High dimensionality | Shrinkage methods | Model selection | Oracle property

Journal Article

Annals of Statistics, ISSN 0090-5364, 04/2010, Volume 38, Issue 2, pp. 635 - 669

A new multivariate concept of quantile, based on a directional version of Koenker and Bassett’s traditional regression quantiles, is introduced for...

Quantile regression | Halfspace depth | Multivariate quantile | quantile regression | 62H05 | halfspace depth | 62J05

Quantile regression | Halfspace depth | Multivariate quantile | quantile regression | 62H05 | halfspace depth | 62J05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2008, Volume 36, Issue 4, pp. 1567 - 1594

Meinshausen and Buhlmann [Ann. Statist. 34 (2006) 1436-1462] showed that, for neighborhood selection in Gaussian graphical models, under a neighborhood...

Regression coefficients | Variable coefficients | Linear regression | Eigenvalues | Neighborhood conditions | Mathematical vectors | Matrices | Coefficients | Covariance matrices | Estimators | High-dimensional data | Random matrices | Bias | Penalized regression | Spectral analysis | Rate consistency | Variable selection | DANTZIG SELECTOR | random matrices | high-dimensional data | bias | spectral analysis | RISK | STATISTICS & PROBABILITY | variable selection | rate consistency | penalized regression | LARGER | STATISTICAL ESTIMATION | 62J05 | 62J07 | 62H25

Regression coefficients | Variable coefficients | Linear regression | Eigenvalues | Neighborhood conditions | Mathematical vectors | Matrices | Coefficients | Covariance matrices | Estimators | High-dimensional data | Random matrices | Bias | Penalized regression | Spectral analysis | Rate consistency | Variable selection | DANTZIG SELECTOR | random matrices | high-dimensional data | bias | spectral analysis | RISK | STATISTICS & PROBABILITY | variable selection | rate consistency | penalized regression | LARGER | STATISTICAL ESTIMATION | 62J05 | 62J07 | 62H25

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2007, Volume 35, Issue 5, pp. 2173 - 2192

We study the effective degrees of freedom of the lasso in the framework of Stein's unbiased risk estimation (SURE). We show that the number of nonzero...

Degrees of freedom | Transition points | Estimation theory | Mathematical vectors | Unbiased estimators | Diabetes | Coefficients | Modeling | Parametric models | Estimation methods | SURE | Unbiased estimate | Model selection | LARS algorithm | Lasso | REGRESSION | NONCONCAVE PENALIZED LIKELIHOOD | lasso | SHRINKAGE | degrees of freedom | unbiased estimate | ADAPTIVE MODEL SELECTION | model selection | STATISTICS & PROBABILITY | VARIABLE SELECTION | 62J05 | 90C46 | 62J07

Degrees of freedom | Transition points | Estimation theory | Mathematical vectors | Unbiased estimators | Diabetes | Coefficients | Modeling | Parametric models | Estimation methods | SURE | Unbiased estimate | Model selection | LARS algorithm | Lasso | REGRESSION | NONCONCAVE PENALIZED LIKELIHOOD | lasso | SHRINKAGE | degrees of freedom | unbiased estimate | ADAPTIVE MODEL SELECTION | model selection | STATISTICS & PROBABILITY | VARIABLE SELECTION | 62J05 | 90C46 | 62J07

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2009, Volume 37, Issue 5A, pp. 2178 - 2201

This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high-dimensional models? In...

Regression coefficients | Error rates | Statistical variance | Sample size | False positive errors | Linear regression | Cleaning | Estimators | Genetic screening | Signal detection | Sparsity | Stepwise regression | Lasso | REGRESSION | stepwise regression | CONSISTENCY | APPROXIMATION | STATISTICS & PROBABILITY | sparsity | 62J05 | 62J07 | Stepwise Regression

Regression coefficients | Error rates | Statistical variance | Sample size | False positive errors | Linear regression | Cleaning | Estimators | Genetic screening | Signal detection | Sparsity | Stepwise regression | Lasso | REGRESSION | stepwise regression | CONSISTENCY | APPROXIMATION | STATISTICS & PROBABILITY | sparsity | 62J05 | 62J07 | Stepwise Regression

Journal Article

9.
Full Text
Asymptotic Properties of Bridge Estimators in Sparse High-Dimensional Regression Models

The Annals of Statistics, ISSN 0090-5364, 4/2008, Volume 36, Issue 2, pp. 587 - 613

We study the asymptotic properties of bridge estimators in sparse, high-dimensional, linear regression models when the number of covariates may increase to...

Zero | Sample size | Objective functions | Mathematical constants | Regression analysis | Coefficients | Orthogonality | Estimators | Consistent estimators | Oracles | High-dimensional data | Penalized regression | Asymptotic normality | Oracle property | Variable selection | asymptotic normality | NONCONCAVE PENALIZED LIKELIHOOD | P2/N | BEHAVIOR | high-dimensional data | oracle property | STATISTICS & PROBABILITY | variable selection | NORMALIZATION | PARAMETERS | penalized regression | 2-WAY SEMILINEAR MODEL | 62J05 | 62J07 | 62E20 | 60F05

Zero | Sample size | Objective functions | Mathematical constants | Regression analysis | Coefficients | Orthogonality | Estimators | Consistent estimators | Oracles | High-dimensional data | Penalized regression | Asymptotic normality | Oracle property | Variable selection | asymptotic normality | NONCONCAVE PENALIZED LIKELIHOOD | P2/N | BEHAVIOR | high-dimensional data | oracle property | STATISTICS & PROBABILITY | variable selection | NORMALIZATION | PARAMETERS | penalized regression | 2-WAY SEMILINEAR MODEL | 62J05 | 62J07 | 62E20 | 60F05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2011, Volume 39, Issue 4, pp. 2164 - 2204

We consider the problem of estimating a sparse linear regression vector β * under a Gaussian noise model, for the purpose of both prediction and model...

Minimax | Cauchy Schwarz inequality | Linear regression | Machine learning | Eigenvalues | Matrices | Random variables | Modeling | Estimators | Oracles | Group Lasso | Group sparsity | Minimax risk | Statistical learning | Moment inequality | Oracle inequalities | Penalized least squares | REGRESSION | statistical learning | penalized least squares | DANTZIG SELECTOR | group Lasso | BIAS | minimax risk | STATISTICS & PROBABILITY | VARIABLE SELECTION | LINEAR-MODELS | moment inequality | HETEROGENEITY | RECOVERY | group sparsity | AGGREGATION | Statistics | Statistics Theory | Mathematics | 62F07 | 62J05 | 62C20

Minimax | Cauchy Schwarz inequality | Linear regression | Machine learning | Eigenvalues | Matrices | Random variables | Modeling | Estimators | Oracles | Group Lasso | Group sparsity | Minimax risk | Statistical learning | Moment inequality | Oracle inequalities | Penalized least squares | REGRESSION | statistical learning | penalized least squares | DANTZIG SELECTOR | group Lasso | BIAS | minimax risk | STATISTICS & PROBABILITY | VARIABLE SELECTION | LINEAR-MODELS | moment inequality | HETEROGENEITY | RECOVERY | group sparsity | AGGREGATION | Statistics | Statistics Theory | Mathematics | 62F07 | 62J05 | 62C20

Journal Article

The Annals of Statistics, ISSN 0090-5364, 2/2007, Volume 35, Issue 1, pp. 70 - 91

In functional linear regression, the slope "parameter" is a function. Therefore, in a nonparametric context, it is determined by an infinite number of...

Integers | Regression Analysis | Inverse problems | Linear regression | Eigenvalues | Linear transformations | Principal components analysis | Mathematical functions | Instrumental variables estimation | Data smoothing | Estimators | Dimension reduction | Deconvolution | Nonparametric | Smoothing | Eigenfunction | Eigenvalue | Quadratic regularisation | Linear operator | Minimax optimality | linear operator | eigenfunction | BLIND DECONVOLUTION | STATISTICS & PROBABILITY | quadratic | dimension reduction | nonparametric | principal components analysis | SAMPLE | deconvolution | minimax optimality | regularisation | MODELS | INVERSE PROBLEMS | smoothing | eigenvalue | 62J05 | quadratic regularisation | 62G20

Integers | Regression Analysis | Inverse problems | Linear regression | Eigenvalues | Linear transformations | Principal components analysis | Mathematical functions | Instrumental variables estimation | Data smoothing | Estimators | Dimension reduction | Deconvolution | Nonparametric | Smoothing | Eigenfunction | Eigenvalue | Quadratic regularisation | Linear operator | Minimax optimality | linear operator | eigenfunction | BLIND DECONVOLUTION | STATISTICS & PROBABILITY | quadratic | dimension reduction | nonparametric | principal components analysis | SAMPLE | deconvolution | minimax optimality | regularisation | MODELS | INVERSE PROBLEMS | smoothing | eigenvalue | 62J05 | quadratic regularisation | 62G20

Journal Article

Review of Managerial Science, ISSN 1863-6683, 7/2011, Volume 5, Issue 2, pp. 233 - 262

Currently, companies spend a great deal of effort on Corporate Social Responsibility (CSR) disclosures. CSR disclosure relates to the provision of information...

62J05 | Global reporting initiative | Voluntary disclosure | Corporate Social Responsibility | Business/Management Science, general | Economics / Management Science | Content analysis | MANAGEMENT | LEGITIMACY | CORPORATE SOCIAL-RESPONSIBILITY | PERFORMANCE | INFORMATION | SUSTAINABLE DEVELOPMENT | EUROPE | SALIENCE | ENVIRONMENTAL DISCLOSURES | Studies | Corporate responsibility | Disclosure | Social responsibility

62J05 | Global reporting initiative | Voluntary disclosure | Corporate Social Responsibility | Business/Management Science, general | Economics / Management Science | Content analysis | MANAGEMENT | LEGITIMACY | CORPORATE SOCIAL-RESPONSIBILITY | PERFORMANCE | INFORMATION | SUSTAINABLE DEVELOPMENT | EUROPE | SALIENCE | ENVIRONMENTAL DISCLOSURES | Studies | Corporate responsibility | Disclosure | Social responsibility

Journal Article

The Annals of Statistics, ISSN 0090-5364, 4/2005, Volume 33, Issue 2, pp. 730 - 773

Variable selection in the linear regression model takes many apparent faces from both frequentist and Bayesian standpoints. In this paper we introduce a...

Zero | Regression Methodology | Variable coefficients | Coordinate systems | Regression analysis | Orthogonality | Frequentism | Modeling | Estimators | Consistent estimators | Oracles | Zcut | Penalization | Generalized ridge regression | Ordinary least squares | Model averaging | Stochastic variable selection | Hypervariance | Shrinkage | Model uncertainty | Rescaling | model averaging | rescaling | hypervariance | LEAST ANGLE REGRESSION | STATISTICS & PROBABILITY | ordinary least squares | stochastic variable selection | LINEAR-MODEL SELECTION | PREDICTION | model uncertainty | generalized ridge regression | SUBSET-SELECTION | BOOTSTRAP | ESTIMATORS | CROSS-VALIDATION | penalization | shrinkage | MULTIPLE SHRINKAGE | 62J05 | 62J07

Zero | Regression Methodology | Variable coefficients | Coordinate systems | Regression analysis | Orthogonality | Frequentism | Modeling | Estimators | Consistent estimators | Oracles | Zcut | Penalization | Generalized ridge regression | Ordinary least squares | Model averaging | Stochastic variable selection | Hypervariance | Shrinkage | Model uncertainty | Rescaling | model averaging | rescaling | hypervariance | LEAST ANGLE REGRESSION | STATISTICS & PROBABILITY | ordinary least squares | stochastic variable selection | LINEAR-MODEL SELECTION | PREDICTION | model uncertainty | generalized ridge regression | SUBSET-SELECTION | BOOTSTRAP | ESTIMATORS | CROSS-VALIDATION | penalization | shrinkage | MULTIPLE SHRINKAGE | 62J05 | 62J07

Journal Article

The Annals of Statistics, ISSN 0090-5364, 4/2011, Volume 39, Issue 2, pp. 731 - 771

In high-dimensional linear regression, the goal pursued here is to estimate an unknown regression function using linear combinations of a suitable set of...

Aggregation | Integers | Minimax | Linear regression | Least squares | Coordinate systems | Random variables | Regression analysis | Estimators | Oracles | Sparsity | Lasso | Minimax Rates | Sparsity Oracle Inequalities | Bic | Adaptation | High-Dimensional Regression | High-dimensional regression | DANTZIG SELECTOR | adaptation | RESTRICTED ISOMETRY PROPERTY | RISK | STATISTICS & PROBABILITY | aggregation | sparsity | VARIABLE SELECTION | BOUNDS | sparsity oracle inequalities | minimax rates | BIC | Probability | Mathematics | 62G05 | 62G08 | 62J05 | 62C20 | 62G20

Aggregation | Integers | Minimax | Linear regression | Least squares | Coordinate systems | Random variables | Regression analysis | Estimators | Oracles | Sparsity | Lasso | Minimax Rates | Sparsity Oracle Inequalities | Bic | Adaptation | High-Dimensional Regression | High-dimensional regression | DANTZIG SELECTOR | adaptation | RESTRICTED ISOMETRY PROPERTY | RISK | STATISTICS & PROBABILITY | aggregation | sparsity | VARIABLE SELECTION | BOUNDS | sparsity oracle inequalities | minimax rates | BIC | Probability | Mathematics | 62G05 | 62G08 | 62J05 | 62C20 | 62G20

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2015, Volume 43, Issue 5, pp. 2055 - 2085

In many fields of science, we observe a response variable together with a large number of potential explanatory variables, and would like to be able to...

Sequential hypothesis testing | Martingale theory | False discovery rate (FDR) | Lasso | Permutation methods | Variable selection | permutation methods | CONFIDENCE-INTERVALS | MODEL SELECTION | martingale theory | STATISTICS & PROBABILITY | false discovery rate (FDR) | sequential hypothesis testing | 62J05 | 62F03

Sequential hypothesis testing | Martingale theory | False discovery rate (FDR) | Lasso | Permutation methods | Variable selection | permutation methods | CONFIDENCE-INTERVALS | MODEL SELECTION | martingale theory | STATISTICS & PROBABILITY | false discovery rate (FDR) | sequential hypothesis testing | 62J05 | 62F03

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2006, Volume 34, Issue 5, pp. 2159 - 2179

There has been substantial recent work on methods for estimating the slope function in linear regression for functional data analysis. However, as in the case...

Density estimation | Error rates | Data analysis | Nonparametric Statistical Analysis | Linear regression | Eigenvalues | Mathematical functions | Regression analysis | Data smoothing | Estimators | Consistent estimators | Minimax | Intercept | Eigenvector | Eigenfunction | Eigenvalue | Principal components analysis | Slope | Dimension reduction | Covariance | Smoothing | Optimal convergence rate | Bootstrap | Spectral decomposition | Rate of convergence | Functional data analysis | spectral decomposition | LOGISTIC-REGRESSION | eigenfunction | rate of convergence | functional data analysis | STATISTICS & PROBABILITY | covariance | optimal convergence rate | bootstrap | slope | dimension reduction | CURVES | principal components analysis | eigenvector | MODELS | intercept | PRINCIPAL-COMPONENTS-ANALYSIS | smoothing | eigenvalue | minimax | 62J05 | 62G20

Density estimation | Error rates | Data analysis | Nonparametric Statistical Analysis | Linear regression | Eigenvalues | Mathematical functions | Regression analysis | Data smoothing | Estimators | Consistent estimators | Minimax | Intercept | Eigenvector | Eigenfunction | Eigenvalue | Principal components analysis | Slope | Dimension reduction | Covariance | Smoothing | Optimal convergence rate | Bootstrap | Spectral decomposition | Rate of convergence | Functional data analysis | spectral decomposition | LOGISTIC-REGRESSION | eigenfunction | rate of convergence | functional data analysis | STATISTICS & PROBABILITY | covariance | optimal convergence rate | bootstrap | slope | dimension reduction | CURVES | principal components analysis | eigenvector | MODELS | intercept | PRINCIPAL-COMPONENTS-ANALYSIS | smoothing | eigenvalue | minimax | 62J05 | 62G20

Journal Article

The Annals of Statistics, ISSN 0090-5364, 4/2014, Volume 42, Issue 2, pp. 413 - 468

In the sparse linear regression setting, we consider testing the significance of the predictor variable that enters the current lasso model, in the sequence of...

Regression coefficients | Statistical variance | Null hypothesis | Degrees of freedom | Covariance | Variable coefficients | Linear regression | P values | Modeling | Covariance matrices | Significance test | P-value | Lasso | Least angle regression | REGRESSION | p-value | ALGORITHM | STATISTICS & PROBABILITY | PARAMETERS | least angle regression | VARIABLE SELECTION | RECOVERY | DIMENSIONAL LINEAR-MODELS | significance test | SHRINKAGE | FREEDOM | REGULARIZATION | 62J05 | 62J07 | 62F03

Regression coefficients | Statistical variance | Null hypothesis | Degrees of freedom | Covariance | Variable coefficients | Linear regression | P values | Modeling | Covariance matrices | Significance test | P-value | Lasso | Least angle regression | REGRESSION | p-value | ALGORITHM | STATISTICS & PROBABILITY | PARAMETERS | least angle regression | VARIABLE SELECTION | RECOVERY | DIMENSIONAL LINEAR-MODELS | significance test | SHRINKAGE | FREEDOM | REGULARIZATION | 62J05 | 62J07 | 62F03

Journal Article

The Annals of Statistics, ISSN 0090-5364, 4/2009, Volume 37, Issue 2, pp. 673 - 696

We consider the problem of simultaneous variable selection and estimation in partially linear models with a divergent number of covariates in the linear part,...

Linear regression | Asymptotic properties | Polynomials | Standard error | Semiparametric modeling | Linear models | Estimators | Consistent estimators | Oracles | Perceptron convergence procedure | High-dimensional data | Semiparametric models | Asymptotic normality | Oracle property | Penalized estimation | Variable selection | RATES | penalized estimation | ESTIMATORS | high-dimensional data | oracle property | LASSO | semiparametric models | CONVERGENCE | STATISTICS & PROBABILITY | variable selection | LIKELIHOOD | 62J05 | 62G08 | 62E20

Linear regression | Asymptotic properties | Polynomials | Standard error | Semiparametric modeling | Linear models | Estimators | Consistent estimators | Oracles | Perceptron convergence procedure | High-dimensional data | Semiparametric models | Asymptotic normality | Oracle property | Penalized estimation | Variable selection | RATES | penalized estimation | ESTIMATORS | high-dimensional data | oracle property | LASSO | semiparametric models | CONVERGENCE | STATISTICS & PROBABILITY | variable selection | LIKELIHOOD | 62J05 | 62G08 | 62E20

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2010, Volume 38, Issue 5, pp. 2587 - 2619

This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when,...

Datasets | Maximum likelihood estimation | Approximation | Empiricism | Bayesian networks | Parametric models | Bayesian analysis | Modeling | Probabilities | Oracles | Empirical Bayes | Bayesian model selection | Multiple testing | Variable selection | PRIORS | empirical Bayes | multiple testing | GROWTH | STATISTICS & PROBABILITY | variable selection | GAUSSIAN GRAPHICAL MODELS | 62J15 | 62J05

Datasets | Maximum likelihood estimation | Approximation | Empiricism | Bayesian networks | Parametric models | Bayesian analysis | Modeling | Probabilities | Oracles | Empirical Bayes | Bayesian model selection | Multiple testing | Variable selection | PRIORS | empirical Bayes | multiple testing | GROWTH | STATISTICS & PROBABILITY | variable selection | GAUSSIAN GRAPHICAL MODELS | 62J15 | 62J05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 4/2013, Volume 41, Issue 2, pp. 802 - 837

It is common practice in statistical data analysis to perform data-driven variable selection and derive statistical inference from the resulting model. Such...

Regression coefficients | Approximation | Inference | Coordinate systems | Mathematical constants | Mathematical vectors | Induced substructures | Modeling | Parametric models | Estimators | Family-wise error | Multiple comparison | High-dimensional inference | Linear regression | Model selection | Sphere packing | CONFIDENCE-INTERVALS | CONDITIONAL LEVEL | multiple comparison | PROPERTY | model selection | STATISTICS & PROBABILITY | MAXIMUM-LIKELIHOOD ESTIMATORS | family-wise error | sphere packing | STUDENTS | high-dimensional inference | MODEL-SELECTION | LASSO | GAUSSIAN REGRESSION | 62J15 | 62J05

Regression coefficients | Approximation | Inference | Coordinate systems | Mathematical constants | Mathematical vectors | Induced substructures | Modeling | Parametric models | Estimators | Family-wise error | Multiple comparison | High-dimensional inference | Linear regression | Model selection | Sphere packing | CONFIDENCE-INTERVALS | CONDITIONAL LEVEL | multiple comparison | PROPERTY | model selection | STATISTICS & PROBABILITY | MAXIMUM-LIKELIHOOD ESTIMATORS | family-wise error | sphere packing | STUDENTS | high-dimensional inference | MODEL-SELECTION | LASSO | GAUSSIAN REGRESSION | 62J15 | 62J05

Journal Article

No results were found for your search.

Cannot display more than 1000 results, please narrow the terms of your search.