# Introduction to High-Dimensional Statistics by Christophe Giraud (Hardback, 2014)

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- Author(s)Christophe Giraud
- PublisherApple Academic Press Inc.
- Date of Publication30/12/2014
- Language(s)English
- FormatHardback
- ISBN-101482237946
- ISBN-139781482237948
- GenreMathematics
- Series TitleChapman & Hall/CRC Monographs on Statistics & Applied Probability
- Series Part/Volume Number138

- Place of PublicationOakville
- Country of PublicationCanada
- ImprintApple Academic Press Inc.
- Content Note33 black & white illustrations, 2 black & white tables

- Weight521 g
- Width156 mm
- Height235 mm
- Pagination270

- Format DetailsUnsewn / adhesive bound

- Table Of ContentsPreface Acknowledgments Introduction High-Dimensional Data Curse of Dimensionality Lost in the Immensity of High-Dimensional Spaces Fluctuations Cumulate An Accumulation of Rare Events May Not Be Rare Computational Complexity High-Dimensional Statistics Circumventing the Curse of Dimensionality A Paradigm Shift Mathematics of High-Dimensional Statistics About This Book Statistics and Data Analysis Purpose of This Book Overview Discussion and References Take-Home Message References Exercises Strange Geometry of High-Dimensional Spaces Volume of a p-Dimensional Ball Tails of a Standard Gaussian Distribution Principal Component Analysis Basics of Linear Regression Concentration of the Square Norm of a Gaussian Random Variable Model Selection Statistical Setting To Select among a Collection of Models Models and Oracle Model Selection Procedures Risk Bound for Model Selection Oracle Risk Bound Optimality Minimax Optimality Frontier of Estimation in High Dimensions Minimal Penalties Computational Issues Illustration An Alternative Point of View on Model Selection Discussion and References Take-Home Message References Exercises Orthogonal Design Risk Bounds for the Different Sparsity Settings Collections of Nested Models Segmentation with Dynamic Programming Goldenshluger-Lepski Method Minimax Lower Bounds Aggregation of Estimators Introduction Gibbs Mixing of Estimators Oracle Risk Bound Numerical Approximation by Metropolis-Hastings Numerical Illustration Discussion and References Take-Home Message References Exercises Gibbs Distribution Orthonormal Setting with Power Law Prior Group-Sparse Setting Gain of Combining Online Aggregation Convex Criteria Reminder on Convex Multivariate Functions Subdifferentials Two Useful Properties Lasso Estimator Geometric Insights Analytic Insights Oracle Risk Bound Computing the Lasso Estimator Removing the Bias of the Lasso Estimator Convex Criteria for Various Sparsity Patterns Group-Lasso (Group Sparsity) Sparse-Group Lasso (Sparse-Group Sparsity) Fused-Lasso (Variation Sparsity) Discussion and References Take-Home Message References Exercises When Is the Lasso Solution Unique? Support Recovery via the Witness Approach Lower Bound on the Compatibility Constant On the Group-Lasso Dantzig Selector Projection on the l1-Ball Ridge and Elastic-Net Estimator Selection Estimator Selection Cross-Validation Techniques Complexity Selection Techniques Coordinate-Sparse Regression Group-Sparse Regression Multiple Structures Scaled-Invariant Criteria References and Discussion Take-Home Message References Exercises Expected V-Fold CV l2-Risk Proof of Corollary 5.5 Some Properties of Penalty (5.4) Selecting the Number of Steps for the Forward Algorithm Multivariate Regression Statistical Setting A Reminder on Singular Values Low-Rank Estimation If We Knew the Rank of A* When the Rank of A* Is Unknown Low Rank and Sparsity Row-Sparse Matrices Criterion for Row-Sparse and Low-Rank Matrices Convex Criterion for Low Rank Matrices Convex Criterion for Sparse and Low-Rank Matrices Discussion and References Take-Home Message References Exercises Hard-Thresholding of the Singular Values Exact Rank Recovery Rank Selection with Unknown Variance Graphical Models Reminder on Conditional Independence Graphical Models Directed Acyclic Graphical Models Nondirected Models Gaussian Graphical Models (GGM) Connection with the Precision Matrix and the Linear Regression Estimating g by Multiple Testing Sparse Estimation of the Precision Matrix Estimation of g by Regression Practical Issues Discussion and References Take-Home M
- Author BiographyChristophe Giraud was a student of the Ecole Normale Superieure de Paris, and he received a Ph.D in probability theory from the University Paris 6. He was assistant professor at the University of Nice from 2002 to 2008. He has been associate professor at the Ecole Polytechnique since 2008 and professor at Paris Sud University (Orsay) since 2012. His current research focuses mainly on the statistical theory of high-dimensional data analysis and its applications to life sciences.

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