Fundamentals of High-Dimensional Statistics: With Exercises and R Labs, Lederer Johannes
Автор: Peter B?hlmann; Sara van de Geer Название: Statistics for High-Dimensional Data ISBN: 3642268579 ISBN-13(EAN): 9783642268571 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This valuable compendium of statistical methods features a unique combination of methodology, theory, algorithms and applications. It covers recently developed approaches to handling large and complex data sets, including the Lasso and boosting methods.
Автор: B?hlmann Название: Statistics for High Dimensional Data ISBN: 3642201911 ISBN-13(EAN): 9783642201912 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This valuable compendium of statistical methods features a unique combination of methodology, theory, algorithms and applications. It covers recently developed approaches to handling large and complex data sets, including the Lasso and boosting methods.
Автор: Karl K. Sabelfeld, Nikolai A. Simonov Название: Stochastic Methods for Boundary Value Problems: Numerics for High-dimensional PDEs and Applications ISBN: 3110479060 ISBN-13(EAN): 9783110479065 Издательство: Walter de Gruyter Рейтинг: Цена: 123910.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This monograph is devoted to random walk based stochastic algorithms for solving high-dimensional boundary value problems of mathematical physics and chemistry. It includes Monte Carlo methods where the random walks live not only on the boundary, but also inside the domain. A variety of examples from capacitance calculations to electron dynamics in semiconductors are discussed to illustrate the viability of the approach.The book is written for mathematicians who work in the field of partial differential and integral equations, physicists and engineers dealing with computational methods and applied probability, for students and postgraduates studying mathematical physics and numerical mathematics. Contents: IntroductionRandom walk algorithms for solving integral equationsRandom walk-on-boundary algorithms for the Laplace equationWalk-on-boundary algorithms for the heat equationSpatial problems of elasticityVariants of the random walk on boundary for solving stationary potential problemsSplitting and survival probabilities in random walk methods and applicationsA random WOS-based KMC method for electron-hole recombinationsMonte Carlo methods for computing macromolecules properties and solving related problemsBibliography
Автор: Koch Название: Analysis of Multivariate and High-Dimensional Data ISBN: 0521887933 ISBN-13(EAN): 9780521887939 Издательство: Cambridge Academ Рейтинг: Цена: 70750.00 T Наличие на складе: Поставка под заказ. Описание: `Big data` poses challenges that require both classical multivariate methods and modern machine-learning techniques. This coherent treatment integrates theory with data analysis, visualisation and interpretation of the analysis. Problems, data sets and MATLAB (R) code complete the package. It is suitable for master`s/graduate students in statistics and working scientists in data-rich disciplines.
Автор: Giraud Christophe Название: Introduction to High-Dimensional Statistics ISBN: 0367716224 ISBN-13(EAN): 9780367716226 Издательство: Taylor&Francis Рейтинг: Цена: 83690.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities.
Автор: Aneiros Germбn, Horovб Ivana, Huskovб Marie Название: Functional and High-Dimensional Statistics and Related Fields ISBN: 3030477584 ISBN-13(EAN): 9783030477585 Издательство: Springer Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Preface.- List of Contributors .- 1 An introduction to the (postponed) 5th edition of the International Workshop on Functional and Operatorial Statistics.- 2 Analysis of Telecom Italia Mobile Phone Data by Space-time Regression with Differential Regularization.- 3 Some Numerical Test on the Convergence Rates of Regression with Differential Regularization.- 4 Learning with Signatures.- 5 About the Complexity Function in Small-ball Probability Factorization.- 6 Principal Components Analysis of a Cyclostationary Random Function.- 7 Level Set and Density Estimation on Manifolds.- 8 Pseudo-metrics as Interesting Tool in Nonparametric Functional Regression.- 9 Testing a Specification Form in Single Functional Index Model.- 10 A New Method for Ordering Functional Data and its Application to Diagnostic Test.- 11 A Functional Data Analysis Approach to the Estimation of Densities over Complex Regions.- 12 A Conformal Approach for Distribution-free Prediction of Functional Data.- 13 G-Lasso Network Analysis for Functional Data.- 14 Modelling Functional Data with High-dimensional Error Structure.- 15 Goodness-of-fit Tests for Functional Linear Models Based on Integrated Projections.- 16 From High-dimensional to Functional Data: Stringing Via Manifold Learning.- 17 Functional Two-sample Tests Based on Empirical Characteristic Functionals.- 18 Some Remarks on the Nelson-Siegel Model.- 19 Modeling the Effect of Recurrent Events on Time-to-event Processes by Means of Functional Data.- 20 On Robust Training of Regression Neural Networks.- 21 Simultaneous Inference for Function-valued Parameters: a Fast and Fair Approach.- 22 Single Functional Index Model under Responses MAR and Dependent Observations.- 23 O2S2 for the Geodata Deluge .- 24 Riemannian Distances between Covariance Operators and Gaussian Processes.- 25 Depth in Infinite-dimensional Spaces.- 26 Variable Selection in Semiparametric Bi-functional Models.- 27 Local Inference for Functional Data Controlling the Functional False Discovery Rate.- 28 Optimum Scale Selection for 3D Point Cloud Classification through Distance Correlation.- 29 Generalized Functional Partially Linear Single-index Models.- 30 Functional Outlier Detection through Probabilistic Modelling.- 31 Topological Object Data Analysis Methods with an Application to Medical Imaging .- 32 Distribution-free Pointwise Adjusted %-values for Functional Hypotheses.- Authors Index.
Автор: Aneiros Germбn, Horovб Ivana, Huskovб Marie Название: Functional and High-Dimensional Statistics and Related Fields ISBN: 303047755X ISBN-13(EAN): 9783030477554 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Preface.- List of Contributors .- 1 An introduction to the (postponed) 5th edition of the International Workshop on Functional and Operatorial Statistics.- 2 Analysis of Telecom Italia Mobile Phone Data by Space-time Regression with Differential Regularization.- 3 Some Numerical Test on the Convergence Rates of Regression with Differential Regularization.- 4 Learning with Signatures.- 5 About the Complexity Function in Small-ball Probability Factorization.- 6 Principal Components Analysis of a Cyclostationary Random Function.- 7 Level Set and Density Estimation on Manifolds.- 8 Pseudo-metrics as Interesting Tool in Nonparametric Functional Regression.- 9 Testing a Specification Form in Single Functional Index Model.- 10 A New Method for Ordering Functional Data and its Application to Diagnostic Test.- 11 A Functional Data Analysis Approach to the Estimation of Densities over Complex Regions.- 12 A Conformal Approach for Distribution-free Prediction of Functional Data.- 13 G-Lasso Network Analysis for Functional Data.- 14 Modelling Functional Data with High-dimensional Error Structure.- 15 Goodness-of-fit Tests for Functional Linear Models Based on Integrated Projections.- 16 From High-dimensional to Functional Data: Stringing Via Manifold Learning.- 17 Functional Two-sample Tests Based on Empirical Characteristic Functionals.- 18 Some Remarks on the Nelson-Siegel Model.- 19 Modeling the Effect of Recurrent Events on Time-to-event Processes by Means of Functional Data.- 20 On Robust Training of Regression Neural Networks.- 21 Simultaneous Inference for Function-valued Parameters: a Fast and Fair Approach.- 22 Single Functional Index Model under Responses MAR and Dependent Observations.- 23 O2S2 for the Geodata Deluge .- 24 Riemannian Distances between Covariance Operators and Gaussian Processes.- 25 Depth in Infinite-dimensional Spaces.- 26 Variable Selection in Semiparametric Bi-functional Models.- 27 Local Inference for Functional Data Controlling the Functional False Discovery Rate.- 28 Optimum Scale Selection for 3D Point Cloud Classification through Distance Correlation.- 29 Generalized Functional Partially Linear Single-index Models.- 30 Functional Outlier Detection through Probabilistic Modelling.- 31 Topological Object Data Analysis Methods with an Application to Medical Imaging .- 32 Distribution-free Pointwise Adjusted %-values for Functional Hypotheses.- Authors Index.
Автор: Giraud Название: Introduction to High-Dimensional Statistics ISBN: 1482237946 ISBN-13(EAN): 9781482237948 Издательство: Taylor&Francis Рейтинг: Цена: 64300.00 T Наличие на складе: Нет в наличии. Описание: Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.
Автор: Caccavale Fabrizio, Ott Christian, Winkler Bernd Название: Bringing Innovative Robotic Technologies from Research Labs to Industrial End-Users: The Experience of the European Robotics Challenges ISBN: 3030345092 ISBN-13(EAN): 9783030345099 Издательство: Springer Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents the main achievements of the EuRoC (European Robotics Challenges) project, which ran from 1st January,2014 to 30th June 2018 and was funded by the European Union under the 7th Framework Programme.
Автор: Caccavale Fabrizio, Ott Christian, Winkler Bernd Название: Bringing Innovative Robotic Technologies from Research Labs to Industrial End-Users: The Experience of the European Robotics Challenges ISBN: 3030345068 ISBN-13(EAN): 9783030345068 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents the main achievements of the EuRoC (European Robotics Challenges) project, which ran from 1st January,2014 to 30th June 2018 and was funded by the European Union under the 7th Framework Programme.
Автор: Kunihiro Suzuki Название: Statistics: Volume 1 -- The Fundamentals ISBN: 1536144622 ISBN-13(EAN): 9781536144628 Издательство: Nova Science Рейтинг: Цена: 290390.00 T Наличие на складе: Невозможна поставка. Описание: We utilize statistics in our daily lives when we evaluate TV program ratings, predict voting outcomes, prepare stock, predict the amounts of sales, and evaluate the effectiveness of medical treatment. We predict the result not on the basis of personal experience, but on the basis of data. However, the accuracy of the prediction depends on the data, the theory, and the depth of understanding the model. In this book, the author analyzes fundamental models to advanced models without skipping their derivation processes. It is then possible to clearly understand the assumption and approximations used in the model, and hence understand the limitation of the model. We also cover almost all of the subjects in statistics since they are all related to each other. Although this book treats advanced models, people who are not professional in science can easily understand the content since by stepping up the derivation from the fundamental level to the advanced level. The author does hope that readers can understand the meaning of the models in statistics and techniques to reach the final results.
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