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The Annals of statistics, ISSN 0090-5364, 02/2009, Volume 37, Issue 1, pp. 246 - 270

The Lasso is an attractive technique for regularization and variable selection for high-dimensional data, where the number of predictor variables $p_{n}$ is...

Approximation | High dimensional spaces | Variable coefficients | Linear regression | Eigenvalues | Signal noise | Mathematical vectors | Estimators | Consistent estimators | Resonance lines | High-dimensional data | Shrinkage estimation | Sparsity | Lasso | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Regression analysis | Normal distribution | Eigen values | Convergence | 62F07 | sparsity | lasso | 62J07 | high-dimensional data

Approximation | High dimensional spaces | Variable coefficients | Linear regression | Eigenvalues | Signal noise | Mathematical vectors | Estimators | Consistent estimators | Resonance lines | High-dimensional data | Shrinkage estimation | Sparsity | Lasso | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Regression analysis | Normal distribution | Eigen values | Convergence | 62F07 | sparsity | lasso | 62J07 | high-dimensional data

Journal Article

The Annals of statistics, ISSN 0090-5364, 06/2006, Volume 34, Issue 3, pp. 1436 - 1462

The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between...

Maximum likelihood estimation | Gaussian distributions | Neighborhoods | Covariance | Linear regression | Connectivity | Random variables | Covariance matrices | Consistent estimators | Oracles | Graphical Model Methods in Statistics | Gaussian graphical models | Penalized regression | Covariance selection | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Mathematical models | Statistical analysis | Estimating techniques | Regression analysis | 62J07 | covariance selection | 62H20 | 62F12 | penalized regression

Maximum likelihood estimation | Gaussian distributions | Neighborhoods | Covariance | Linear regression | Connectivity | Random variables | Covariance matrices | Consistent estimators | Oracles | Graphical Model Methods in Statistics | Gaussian graphical models | Penalized regression | Covariance selection | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Mathematical models | Statistical analysis | Estimating techniques | Regression analysis | 62J07 | covariance selection | 62H20 | 62F12 | penalized regression

Journal Article

The annals of applied statistics, ISSN 1932-6157, 12/2010, Volume 4, Issue 4, pp. 2049 - 2072

When choosing a suitable technique for regression and classification with multivariate predictor variables, one is often faced with a tradeoff between...

Trees | Datasets | Statistical variance | Tree growth | Interpretability | Machine learning | Climate models | Tree felling | Weighted averages | Estimators | Random Forests | Tree ensembles | Sparsity | Quadratic programming | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | sparsity | tree ensembles | machine learning | quadratic programming

Trees | Datasets | Statistical variance | Tree growth | Interpretability | Machine learning | Climate models | Tree felling | Weighted averages | Estimators | Random Forests | Tree ensembles | Sparsity | Quadratic programming | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | sparsity | tree ensembles | machine learning | quadratic programming

Journal Article

Nature (London), ISSN 1476-4687, 04/2009, Volume 458, Issue 7242, pp. 1163 - 1166

Global efforts to mitigate climate change are guided by projections of future temperatures. But the eventual equilibrium global mean temperature associated...

Science & Technology - Other Topics | Multidisciplinary Sciences | Science & Technology | Models, Theoretical | Human Activities - history | Temperature | Uncertainty | History, 21st Century | History, 20th Century | Greenhouse Effect | Benchmarking | History, 18th Century | Carbon Dioxide - analysis | Time Factors | Computer Simulation | Industry - history | History, 19th Century | Atmosphere - chemistry | Carbon - analysis | Control | Emissions (Pollution) | Carbon dioxide | Environmental aspects | Causes of | Influence | Global warming | Research | Confidence intervals | Methods | Emissions | Constraints | Computer simulation | Pathways | Stabilization | Emission | Projection | Carbon | Index Medicus

Science & Technology - Other Topics | Multidisciplinary Sciences | Science & Technology | Models, Theoretical | Human Activities - history | Temperature | Uncertainty | History, 21st Century | History, 20th Century | Greenhouse Effect | Benchmarking | History, 18th Century | Carbon Dioxide - analysis | Time Factors | Computer Simulation | Industry - history | History, 19th Century | Atmosphere - chemistry | Carbon - analysis | Control | Emissions (Pollution) | Carbon dioxide | Environmental aspects | Causes of | Influence | Global warming | Research | Confidence intervals | Methods | Emissions | Constraints | Computer simulation | Pathways | Stabilization | Emission | Projection | Carbon | Index Medicus

Journal Article

Journal of machine learning research, ISSN 1533-7928, 06/2006, Volume 7, pp. 983 - 999

Random forests were introduced as a machine learning tool in Breiman (2001) and have since proven to be very popular and powerful for high-dimensional...

Quantile regression | Adaptive neighborhood regression | Random forests | Automation & Control Systems | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology

Quantile regression | Adaptive neighborhood regression | Random forests | Automation & Control Systems | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology

Journal Article

The Annals of statistics, ISSN 0090-5364, 2/2006, Volume 34, Issue 1, pp. 373 - 393

We consider the problem of estimating the number of false null hypotheses among a very large number of independently tested hypotheses, focusing on the...

Error rates | Null hypothesis | Higher criticism | Proportions | Kuiper belt | P values | Multiple Testing Problems | Occultation | Kuiper belt objects | Distribution functions | Estimation methods | Hypothesis testing | Sparsity | Multiple comparisons | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Estimating techniques | 62H15 | 62J15 | sparsity | multiple comparisons | 62P35

Error rates | Null hypothesis | Higher criticism | Proportions | Kuiper belt | P values | Multiple Testing Problems | Occultation | Kuiper belt objects | Distribution functions | Estimation methods | Hypothesis testing | Sparsity | Multiple comparisons | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Estimating techniques | 62H15 | 62J15 | sparsity | multiple comparisons | 62P35

Journal Article

Journal of the Royal Statistical Society. Series B, Statistical methodology, ISSN 1369-7412, 07/2010, Volume 72, Issue 4, pp. 417 - 473

Estimation of structure, such as in variable selection, graphical modelling or cluster analysis, is notoriously difficult, especially for high dimensional...

Datasets | Regression coefficients | False positive errors | Linear regression | Machine learning | Eigenvalues | Mathematical independent variables | Gene expression | Modeling | Linear models | Structure estimation | Stability selection | High dimensional data | Resampling | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Analysis | Algorithms | Statistical data | Studies | Cluster analysis | Estimating techniques | Research methodology | Sampling techniques | Stability | Mathematical analysis | Samples | Consistency | Gaussian | Mathematical models | Modelling | Models

Datasets | Regression coefficients | False positive errors | Linear regression | Machine learning | Eigenvalues | Mathematical independent variables | Gene expression | Modeling | Linear models | Structure estimation | Stability selection | High dimensional data | Resampling | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Analysis | Algorithms | Statistical data | Studies | Cluster analysis | Estimating techniques | Research methodology | Sampling techniques | Stability | Mathematical analysis | Samples | Consistency | Gaussian | Mathematical models | Modelling | Models

Journal Article

The Annals of statistics, ISSN 0090-5364, 8/2015, Volume 43, Issue 4, pp. 1801 - 1830

Large-scale data are often characterized by some degree of inhomogeneity as data are either recorded in different time regimes or taken from multiple sources....

Aggregation | Big data | Robustness | Regularization | Mixture models | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Mathematical problems | Theoretical mathematics | Mathematical models | Regression analysis | Estimating techniques | Random variables | Statistics - Methodology | robustness | aggregation | big data | 62J07 | regularization

Aggregation | Big data | Robustness | Regularization | Mixture models | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Mathematical problems | Theoretical mathematics | Mathematical models | Regression analysis | Estimating techniques | Random variables | Statistics - Methodology | robustness | aggregation | big data | 62J07 | regularization

Journal Article

Computational statistics & data analysis, ISSN 0167-9473, 09/2007, Volume 52, Issue 1, pp. 374 - 393

The Lasso is an attractive regularisation method for high-dimensional regression. It combines variable selection with an efficient computational procedure....

Dimensionality reduction | High dimensionality | Bridge estimation | [formula omitted]-norm penalisation | Lasso | norm penalisation | Statistics & Probability | Physical Sciences | Computer Science, Interdisciplinary Applications | Technology | Computer Science | Mathematics | Science & Technology

Dimensionality reduction | High dimensionality | Bridge estimation | [formula omitted]-norm penalisation | Lasso | norm penalisation | Statistics & Probability | Physical Sciences | Computer Science, Interdisciplinary Applications | Technology | Computer Science | Mathematics | Science & Technology

Journal Article

The Annals of statistics, ISSN 0090-5364, 8/2012, Volume 40, Issue 4, pp. 1973 - 1977

The authors want to congratulate the authors for a thought-provoking and very interesting paper. Sparse modeling of the concentration matrix has enjoyed...

Factor analysis | Algebra | Hidden variables | Computer software | Covariance matrices | Estimators | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Matrix | Random variables | Normal distribution | Convex analysis

Factor analysis | Algebra | Hidden variables | Computer software | Covariance matrices | Estimators | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Matrix | Random variables | Normal distribution | Convex analysis

Journal Article

Journal of the Royal Statistical Society. Series B, Statistical methodology, ISSN 1369-7412, 11/2015, Volume 77, Issue 5, pp. 923 - 945

It is in general challenging to provide confidence intervals for individual variables in high dimensional regression without making strict or unverifiable...

Preliminary estimates | Regression coefficients | Null hypothesis | Sample size | Mathematical constants | Mathematical vectors | Estimators | Mathematical expressions | Confidence interval | Vertices | Group test | Linear programming | Basis pursuit | Lasso | Ridge | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Statistical analysis | Regression analysis

Preliminary estimates | Regression coefficients | Null hypothesis | Sample size | Mathematical constants | Mathematical vectors | Estimators | Mathematical expressions | Confidence interval | Vertices | Group test | Linear programming | Basis pursuit | Lasso | Ridge | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Statistical analysis | Regression analysis

Journal Article

Nature (London), ISSN 1476-4687, 04/2009, Volume 458, Issue 7242, pp. 1158 - 1162

[...] IPCC AR4 Working Group III23 provided equilibrium warming estimates corresponding to 2100 radiative forcing levels for some multi-gas mitigation...

Control | Emissions (Pollution) | Greenhouses | Causes of | Environmental aspects | Influence | Climatic changes | Global warming | Research | Confidence intervals | Climate change | Temperature | Industrialized nations | Probability | Fossil fuels | Greenhouse gases | Natural gas | Emissions | Climate | Carbon dioxide | Emission | Emissions control | Estimates | Indicators | Constraining

Control | Emissions (Pollution) | Greenhouses | Causes of | Environmental aspects | Influence | Climatic changes | Global warming | Research | Confidence intervals | Climate change | Temperature | Industrialized nations | Probability | Fossil fuels | Greenhouse gases | Natural gas | Emissions | Climate | Carbon dioxide | Emission | Emissions control | Estimates | Indicators | Constraining

Journal Article

Computational statistics, ISSN 0943-4062, 6/2014, Volume 29, Issue 3, pp. 515 - 528

...Comput Stat (2014) 29:515–528
DOI 10.1007/s00180-013-0437-2
ORIGINAL PAPER
Sparse distance metric learning
Tze Choy · Nicolai Meinshausen
Received: 5 September...

Lasso | Consistency | Probability Theory and Stochastic Processes | Economic Theory | Sparse recovery | Statistics, general | Statistics | Multiclass | High-dimensional | Probability and Statistics in Computer Science | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Distance education | Algorithms | Studies | Analysis | Statistical methods | Learning | Least squares method | Mathematical analysis | Classification | Texts | Mathematical models | Transformations

Lasso | Consistency | Probability Theory and Stochastic Processes | Economic Theory | Sparse recovery | Statistics, general | Statistics | Multiclass | High-dimensional | Probability and Statistics in Computer Science | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Distance education | Algorithms | Studies | Analysis | Statistical methods | Learning | Least squares method | Mathematical analysis | Classification | Texts | Mathematical models | Transformations

Journal Article

The Annals of statistics, ISSN 0090-5364, 12/2011, Volume 39, Issue 6, pp. 3369 - 3391

Test statistics are often strongly dependent in large-scale multiple testing applications. Most corrections for multiplicity are unduly conservative for...

Mathematical procedures | Null hypothesis | Determinism | Permutation tests | Sample size | Applied statistics | P values | Random variables | Statistics | Oracles | Permutations | Sparsity | Multiple testing under dependence | Rank-based nonparametric tests | Asymptotic optimality | Westfall-Young procedure | High-dimensional inference | Familywise error rate | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Statistical methods | Asymptotic methods | Dependence | Testing | high-dimensional inference | 62J15 | asymptotic optimality | sparsity | permutations | familywise error rate | rank-based nonparametric tests | 62F03 | Westfall–Young procedure

Mathematical procedures | Null hypothesis | Determinism | Permutation tests | Sample size | Applied statistics | P values | Random variables | Statistics | Oracles | Permutations | Sparsity | Multiple testing under dependence | Rank-based nonparametric tests | Asymptotic optimality | Westfall-Young procedure | High-dimensional inference | Familywise error rate | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Statistical methods | Asymptotic methods | Dependence | Testing | high-dimensional inference | 62J15 | asymptotic optimality | sparsity | permutations | familywise error rate | rank-based nonparametric tests | 62F03 | Westfall–Young procedure

Journal Article

15.
Full Text
Causal inference by using invariant prediction: identification and confidence intervals

Journal of the Royal Statistical Society. Series B, Statistical methodology, ISSN 1369-7412, 11/2016, Volume 78, Issue 5, pp. 947 - 1012

Summary
What is the difference between a prediction that is made with a causal model and that with a non‐causal model? Suppose that we intervene on the...

Causal inference | Confidence intervals | Invariant prediction | Causal discovery | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Statistics | Perturbation methods | Mathematical analysis | Inference | Mathematical models | Robustness | Invariance | Invariants

Causal inference | Confidence intervals | Invariant prediction | Causal discovery | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Statistics | Perturbation methods | Mathematical analysis | Inference | Mathematical models | Robustness | Invariance | Invariants

Journal Article