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Publication Date1998, Statistics, textbooks and monographs, ISBN 0824701569, Volume 156, xii, 632
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2014, SpringerBriefs in statistics, ISBN 9783319031484, ix, 115
The objective of this book is to compare the statistical properties of penalty and non-penalty estimation strategies for some popular models. Specifically, it...
Mathematical statistics | Statistics
Mathematical statistics | Statistics
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Statistica Sinica, ISSN 1017-0405, 1/2013, Volume 23, Issue 1, pp. 119 - 143
We propose a generalized double Pareto prior for Bayesian shrinkage estimation and inferences in linear models. The prior can be obtained via a scale mixture...
Density estimation | A posteriori knowledge | Threshing | Analytical estimating | Machine learning | General | Standard error | Modeling | Frequentism | Estimators | Horseshoes | High-dimensional data | Maximum a posteriori estimation | Robust prior | LASSO | Heavy tails | Shrinkage estimation | Relevance vector machine | relevance vector machine | REGRESSION | robust prior | high-dimensional data | STATISTICS & PROBABILITY | shrinkage estimation | ESTIMATOR | INFERENCE | VARIABLE SELECTION | MIXTURES | NONCONCAVE PENALIZED LIKELIHOOD | maximum a posteriori estimation | ASYMPTOTICS
Density estimation | A posteriori knowledge | Threshing | Analytical estimating | Machine learning | General | Standard error | Modeling | Frequentism | Estimators | Horseshoes | High-dimensional data | Maximum a posteriori estimation | Robust prior | LASSO | Heavy tails | Shrinkage estimation | Relevance vector machine | relevance vector machine | REGRESSION | robust prior | high-dimensional data | STATISTICS & PROBABILITY | shrinkage estimation | ESTIMATOR | INFERENCE | VARIABLE SELECTION | MIXTURES | NONCONCAVE PENALIZED LIKELIHOOD | maximum a posteriori estimation | ASYMPTOTICS
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Loading RightsJournal of the Royal Statistical Society: Series B (Statistical Methodology), ISSN 1369-7412, 09/2019, Volume 81, Issue 4, pp. 781 - 804
Summary We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior...
Stochastic volatility | Dynamic linear model | Trend filtering | Asset pricing | Time series | REGRESSION | HIERARCHICAL SHRINKAGE | HORSESHOE ESTIMATOR | LIKELIHOOD INFERENCE | RISK | STATISTICS & PROBABILITY | Health care industry | Models | Analysis | Pricing | Regression models | Algorithms | Microprocessors | Dependence | Process parameters | Regression analysis | Shrinkage | Risk analysis | Bayesian analysis | Curve fitting | Statistics - Methodology
Stochastic volatility | Dynamic linear model | Trend filtering | Asset pricing | Time series | REGRESSION | HIERARCHICAL SHRINKAGE | HORSESHOE ESTIMATOR | LIKELIHOOD INFERENCE | RISK | STATISTICS & PROBABILITY | Health care industry | Models | Analysis | Pricing | Regression models | Algorithms | Microprocessors | Dependence | Process parameters | Regression analysis | Shrinkage | Risk analysis | Bayesian analysis | Curve fitting | Statistics - Methodology
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Loading RightsJournal of the American Statistical Association, ISSN 0162-1459, 10/2015, Volume 110, Issue 512, pp. 1479 - 1490
Penalized regression methods, such as L 1 regularization, are routinely used in high-dimensional applications, and there is a rich literature on optimality...
Shrinkage prior | Lasso | Bayesian | Convergence rate | Penalized regression | High-dimensional | Regularization | REGRESSION | SCALE MIXTURES | STATISTICS & PROBABILITY | LINEAR-MODELS | DISTRIBUTIONS | L-1 | EMPIRICAL-BAYES | NEEDLES | SPARSE SEQUENCES | VARIABLE-SELECTION | STRAW | High dimensional | L 1
Shrinkage prior | Lasso | Bayesian | Convergence rate | Penalized regression | High-dimensional | Regularization | REGRESSION | SCALE MIXTURES | STATISTICS & PROBABILITY | LINEAR-MODELS | DISTRIBUTIONS | L-1 | EMPIRICAL-BAYES | NEEDLES | SPARSE SEQUENCES | VARIABLE-SELECTION | STRAW | High dimensional | L 1
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Loading Rights2000, ISBN 1560728744, xi, 162
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Journal of the Royal Statistical Society. Series B (Statistical Methodology), ISSN 1369-7412, 6/2011, Volume 73, Issue 3, pp. 273 - 282
In the paper I give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and...
Generalized linear model | Algorithms | Maps | Approximation | Linear regression | Coordinate systems | Regression analysis | Modeling | Oracles | Computational statistics | Penalization | l1‐penalty | Regularization | penalty | SPARSITY | MIXTURE | DANTZIG SELECTOR | LOGISTIC-REGRESSION | STATISTICS & PROBABILITY | l-penalty | ALGORITHMS | GENERALIZED LINEAR-MODELS | VARIABLE SELECTION | MINIMIZATION | COORDINATE DESCENT | Methodology | Regression | Shrinkage
Generalized linear model | Algorithms | Maps | Approximation | Linear regression | Coordinate systems | Regression analysis | Modeling | Oracles | Computational statistics | Penalization | l1‐penalty | Regularization | penalty | SPARSITY | MIXTURE | DANTZIG SELECTOR | LOGISTIC-REGRESSION | STATISTICS & PROBABILITY | l-penalty | ALGORITHMS | GENERALIZED LINEAR-MODELS | VARIABLE SELECTION | MINIMIZATION | COORDINATE DESCENT | Methodology | Regression | Shrinkage
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Loading RightsJournal of the American Statistical Association, ISSN 0162-1459, 06/2009, Volume 104, Issue 486, pp. 747 - 757
The varying coefficient model is a useful extension of the linear regression model. Nevertheless, how to conduct variable selection for the varying coefficient...
Least Absolute Shrinkage and Selection Operator | Oracle property | Bayesian information criterion | Kernel smoothing | Smoothly Clipped Absolute Deviation | Variable selection | Varying coefficient model | Regression coefficients | Estimate reliability | Theory and Methods | Linear regression | Data smoothing | Semiparametric modeling | Modeling | Parametric models | Estimators | Oracles | Estimation methods | REGRESSION | Variable selections | STATISTICS & PROBABILITY | LONGITUDINAL DATA | VARIABLE SELECTION | LINEAR-MODELS | ORACLE PROPERTIES | NONCONCAVE PENALIZED LIKELIHOOD | INFERENCES | ADAPTIVE LASSO | Least absolute deviations (Statistics) | Models | Coefficient of variation | Analysis | Estimation theory
Least Absolute Shrinkage and Selection Operator | Oracle property | Bayesian information criterion | Kernel smoothing | Smoothly Clipped Absolute Deviation | Variable selection | Varying coefficient model | Regression coefficients | Estimate reliability | Theory and Methods | Linear regression | Data smoothing | Semiparametric modeling | Modeling | Parametric models | Estimators | Oracles | Estimation methods | REGRESSION | Variable selections | STATISTICS & PROBABILITY | LONGITUDINAL DATA | VARIABLE SELECTION | LINEAR-MODELS | ORACLE PROPERTIES | NONCONCAVE PENALIZED LIKELIHOOD | INFERENCES | ADAPTIVE LASSO | Least absolute deviations (Statistics) | Models | Coefficient of variation | Analysis | Estimation theory
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Loading RightsAMERICAN JOURNAL OF EPIDEMIOLOGY, ISSN 0002-9262, 02/2018, Volume 187, Issue 2, pp. 358 - 365
We present a method for improving estimation in linear regression models in samples of moderate size, using shrinkage techniques. Our work connects the theory...
confounding factors (epidemiology) | REGRESSION | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | shrinkage estimators | LASSO | model selection | UNCERTAINTY | BAYESIAN-ADJUSTMENT | Epidemiologic Research Design | Smoking - adverse effects | Ethnic Groups - statistics & numerical data | United States - epidemiology | Reproducibility of Results | Carotid Intima-Media Thickness - statistics & numerical data | Bias | Cross-Sectional Studies - methods | Humans | Linear Models | Atherosclerosis - ethnology | Statistics as Topic - methods | Atherosclerosis - epidemiology | Sample Size | Computer Simulation | Bayes Theorem | Practice of Epidemiology
confounding factors (epidemiology) | REGRESSION | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | shrinkage estimators | LASSO | model selection | UNCERTAINTY | BAYESIAN-ADJUSTMENT | Epidemiologic Research Design | Smoking - adverse effects | Ethnic Groups - statistics & numerical data | United States - epidemiology | Reproducibility of Results | Carotid Intima-Media Thickness - statistics & numerical data | Bias | Cross-Sectional Studies - methods | Humans | Linear Models | Atherosclerosis - ethnology | Statistics as Topic - methods | Atherosclerosis - epidemiology | Sample Size | Computer Simulation | Bayes Theorem | Practice of Epidemiology
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Loading Rights01/2017, ISBN 0824701569
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Journal of Econometrics, ISSN 0304-4076, 01/2016, Volume 190, Issue 1, pp. 115 - 132
This paper introduces shrinkage for general parametric models. We show how to shrink maximum likelihood estimators towards parameter subspaces defined by...
Minimax | Nonlinear | Maximum likelihood | James–Stein | ARBITRARY QUADRATIC LOSS | LINEAR-REGRESSION | MINIMAX ESTIMATION | INFORMATION | James-Stein | CONFIDENCE SETS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | ESTIMATORS | MULTIVARIATE NORMAL-DISTRIBUTION | SOCIAL SCIENCES, MATHEMATICAL METHODS | SUBSPACES | ECONOMICS
Minimax | Nonlinear | Maximum likelihood | James–Stein | ARBITRARY QUADRATIC LOSS | LINEAR-REGRESSION | MINIMAX ESTIMATION | INFORMATION | James-Stein | CONFIDENCE SETS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | ESTIMATORS | MULTIVARIATE NORMAL-DISTRIBUTION | SOCIAL SCIENCES, MATHEMATICAL METHODS | SUBSPACES | ECONOMICS
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Loading RightsSkeletal Radiology, ISSN 0364-2348, 1/2018, Volume 47, Issue 1, pp. 61 - 68
Osteochondromas are the most common benign bone tumors, and thus far, their spontaneous shrinkage is considered a rare phenomenon. This study was designed to...
Pathology | Spontaneous regression | Medicine & Public Health | Orthopedics | Osteochondroma | Nuclear Medicine | Imaging / Radiology | Natural history | EVANESCENT EXOSTOSIS | BONE-TUMORS | SPONTANEOUS RESOLUTION | ORTHOPEDICS | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Medicine, Experimental | Medical research | Analysis | Tumors | Growth plate | Absorption | Morphology | Biocompatibility | Remission | Bone tumors | Shrinkage | Regression analysis | Patients
Pathology | Spontaneous regression | Medicine & Public Health | Orthopedics | Osteochondroma | Nuclear Medicine | Imaging / Radiology | Natural history | EVANESCENT EXOSTOSIS | BONE-TUMORS | SPONTANEOUS RESOLUTION | ORTHOPEDICS | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Medicine, Experimental | Medical research | Analysis | Tumors | Growth plate | Absorption | Morphology | Biocompatibility | Remission | Bone tumors | Shrinkage | Regression analysis | Patients
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Loading RightsJournal of the Royal Statistical Society. Series B (Statistical Methodology), ISSN 1369-7412, 6/2014, Volume 76, Issue 3, pp. 627 - 649
High dimensional sparse modelling via regularization provides a powerful tool for analysing large-scale data sets and obtaining meaningful interpretable...
High dimensionality | Prediction and variable selection | Hard thresholding | Global optimality | Shrinkage effect | Thresholded regression | NONCONCAVE PENALIZED LIKELIHOOD | DANTZIG SELECTOR | MODEL SELECTION | LASSO | STATISTICS & PROBABILITY | VARIABLE SELECTION | Studies | Data analysis | Statistical methods | Simulation | Penalty function | Inequalities | Regression | Tools | Risk | Shrinkage | Estimators | Optimization
High dimensionality | Prediction and variable selection | Hard thresholding | Global optimality | Shrinkage effect | Thresholded regression | NONCONCAVE PENALIZED LIKELIHOOD | DANTZIG SELECTOR | MODEL SELECTION | LASSO | STATISTICS & PROBABILITY | VARIABLE SELECTION | Studies | Data analysis | Statistical methods | Simulation | Penalty function | Inequalities | Regression | Tools | Risk | Shrinkage | Estimators | Optimization
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Loading RightsAnnals of the American Association of Geographers, ISSN 2469-4452, 09/2019, Volume 109, Issue 5, pp. 1651 - 1670
The majority of the shrinking cities literature focuses solely on instances of population loss and economic decline. This article argues that shrinking cities...
cambio demográfico | declinación urbana | ciudad en contracción | prosperidad económica | UNITED-STATES | demographic change | TERM | HAPPINESS | VACANT LAND | DECLINE | urban decline | URBAN-POPULATION LOSS | CITY | shrinking city | METROPOLIS | GEOGRAPHY | economic prosperity | AUSTERITY | Economics | Quotients | Prosperity | Regions | Shrinkage | Regression analysis | Industrial areas | Population decline | Coastal zone | Surveys | Demographics | Census | Economic decline | Population | Cities | Deprived urban areas
cambio demográfico | declinación urbana | ciudad en contracción | prosperidad económica | UNITED-STATES | demographic change | TERM | HAPPINESS | VACANT LAND | DECLINE | urban decline | URBAN-POPULATION LOSS | CITY | shrinking city | METROPOLIS | GEOGRAPHY | economic prosperity | AUSTERITY | Economics | Quotients | Prosperity | Regions | Shrinkage | Regression analysis | Industrial areas | Population decline | Coastal zone | Surveys | Demographics | Census | Economic decline | Population | Cities | Deprived urban areas
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Loading RightsEconometric Theory, ISSN 0266-4666, 09/2014, Volume 31, Issue 4, pp. 860 - 879
This paper is an extension of Magnus (2002, Econometrics Journal 5, 225-236) to multiple dimensions. We consider estimation of a multivariate normal mean under...
REGRESSION | JAMES-STEIN ESTIMATOR | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | ADMISSIBLE MINIMAX ESTIMATORS | SPARSE MODELS | MULTIVARIATE NORMAL-DISTRIBUTION | STATISTICS & PROBABILITY | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | SELECTION | FAMILY | Statistical analysis | Estimating techniques | Shrinkage | Econometrics
REGRESSION | JAMES-STEIN ESTIMATOR | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | ADMISSIBLE MINIMAX ESTIMATORS | SPARSE MODELS | MULTIVARIATE NORMAL-DISTRIBUTION | STATISTICS & PROBABILITY | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | SELECTION | FAMILY | Statistical analysis | Estimating techniques | Shrinkage | Econometrics
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Loading RightsMETHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES, ISSN 1614-1881, 04/2019, Volume 15, Issue 2, pp. 88 - 96
Multilevel models have recently been used to empirically investigate the idea that social characteristics are intersectional such as age, sex, ethnicity, and...
intersectionality | COMPLEXITY | Empirical Bayes residuals | SOCIAL SCIENCES, MATHEMATICAL METHODS | PSYCHOLOGY, MATHEMATICAL | dummy variable regression | HEALTH | multilevel models | shrinkage
intersectionality | COMPLEXITY | Empirical Bayes residuals | SOCIAL SCIENCES, MATHEMATICAL METHODS | PSYCHOLOGY, MATHEMATICAL | dummy variable regression | HEALTH | multilevel models | shrinkage
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Loading RightsIEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 08/2017, Volume 28, Issue 8, pp. 1851 - 1864
L-2-rescale boosting (L-2-RBoosting) is a variant of L-2-Boosting, which can essentially improve the generalization performance of L-2-Boosting. The key...
RBoosting | shrinkage degree | rescale boosting (L | Boosting | generalization capability | regression | DIAGNOSIS | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | STRENGTH | ALGORITHMS | CONVEX-OPTIMIZATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | CLASSIFIERS | ENGINEERING, ELECTRICAL & ELECTRONIC | GREEDY APPROXIMATION | LASSO | CONVERGENCE | COMPUTER SCIENCE, THEORY & METHODS | MACHINE | L-2-rescale boosting (L-2-RBoosting)
RBoosting | shrinkage degree | rescale boosting (L | Boosting | generalization capability | regression | DIAGNOSIS | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | STRENGTH | ALGORITHMS | CONVEX-OPTIMIZATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | CLASSIFIERS | ENGINEERING, ELECTRICAL & ELECTRONIC | GREEDY APPROXIMATION | LASSO | CONVERGENCE | COMPUTER SCIENCE, THEORY & METHODS | MACHINE | L-2-rescale boosting (L-2-RBoosting)
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Loading RightsJournal of Food Engineering, ISSN 0260-8774, 01/2013, Volume 114, Issue 1, pp. 99 - 105
► Two different mathematical models for eggplant drying are here proposed. ► The equivalence of the two models is demonstrated under specific conditions. ►...
Shrinkage | Eggplant | Mathematical model | Drying | DEHYDRATED POTATOES | POROSITY | THIN-LAYERS | QUALITY | FOOD SCIENCE & TECHNOLOGY | DENSITY | ENGINEERING, CHEMICAL | SLICES | PHYSICAL-CHARACTERISTICS | PRETREATMENT | KINETICS | MASS-TRANSFER | Mathematical analysis | Moisture content | Regression | Evolution | Mathematical models | Foods
Shrinkage | Eggplant | Mathematical model | Drying | DEHYDRATED POTATOES | POROSITY | THIN-LAYERS | QUALITY | FOOD SCIENCE & TECHNOLOGY | DENSITY | ENGINEERING, CHEMICAL | SLICES | PHYSICAL-CHARACTERISTICS | PRETREATMENT | KINETICS | MASS-TRANSFER | Mathematical analysis | Moisture content | Regression | Evolution | Mathematical models | Foods
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Loading RightsJournal of the Royal Statistical Society. Series B (Statistical Methodology), ISSN 1369-7412, 3/2012, Volume 74, Issue 2, pp. 287 - 311
We use Lévy processes to generate joint prior distributions, and therefore penalty functions, for a location parameter β= (β₁ ,... , β p ) as p grows large....
Datasets | Brownian motion | Statistical variance | Penalty function | Poisons | Gaussian distributions | Bayesian networks | Random variables | Horseshoes | Estimators | Shrinkage | Sparsity | Lévy processes | Partial least squares | Normal scale mixtures | Principal components regression | Levy processes | BAYES | STATISTICS & PROBABILITY | POSTERIOR MOMENTS | MODEL | DISTRIBUTIONS | MIXTURES | NONCONCAVE PENALIZED LIKELIHOOD | VARIABLE-SELECTION | Studies | Statistical methods | Regression analysis | Rule based | Methodology | Computation | Images | Regression | Position (location) | Joints
Datasets | Brownian motion | Statistical variance | Penalty function | Poisons | Gaussian distributions | Bayesian networks | Random variables | Horseshoes | Estimators | Shrinkage | Sparsity | Lévy processes | Partial least squares | Normal scale mixtures | Principal components regression | Levy processes | BAYES | STATISTICS & PROBABILITY | POSTERIOR MOMENTS | MODEL | DISTRIBUTIONS | MIXTURES | NONCONCAVE PENALIZED LIKELIHOOD | VARIABLE-SELECTION | Studies | Statistical methods | Regression analysis | Rule based | Methodology | Computation | Images | Regression | Position (location) | Joints
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