Functional and High-Dimensional Statistics and Related Fields, Aneiros Germбn, Horovб Ivana, Huskovб Marie
Название: Functional statistics and related fields. ISBN: 3319558455 ISBN-13(EAN): 9783319558455 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume collects latest methodological and applied contributions on functional, high-dimensional and other complex data, related statistical models and tools as well as on operator-based statistics.
Автор: Giraud Christophe Название: Introduction to High-Dimensional Statistics ISBN: 0367716224 ISBN-13(EAN): 9780367716226 Издательство: Taylor&Francis Рейтинг: Цена: 12554.00 р. 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.
Описание: This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading.
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: 20962.00 р. 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: B?hlmann Название: Statistics for High Dimensional Data ISBN: 3642201911 ISBN-13(EAN): 9783642201912 Издательство: Springer Рейтинг: Цена: 16769.00 р. 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: Koch Название: Analysis of Multivariate and High-Dimensional Data ISBN: 0521887933 ISBN-13(EAN): 9780521887939 Издательство: Cambridge Academ Рейтинг: Цена: 10613.00 р. 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: Peter B?hlmann; Sara van de Geer Название: Statistics for High-Dimensional Data ISBN: 3642268579 ISBN-13(EAN): 9783642268571 Издательство: Springer Рейтинг: Цена: 16769.00 р. 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: Giraud Название: Introduction to High-Dimensional Statistics ISBN: 1482237946 ISBN-13(EAN): 9781482237948 Издательство: Taylor&Francis Рейтинг: Цена: 9645.00 р. 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.
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: Pons, Odile, Название: Orthonormal series estimators / ISBN: 9811210683 ISBN-13(EAN): 9789811210686 Издательство: World Scientific Publishing Рейтинг: Цена: 14256.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The approximation and the estimation of nonparametric functions by projections on an orthonormal basis of functions are useful in data analysis. This book presents series estimators defined by projections on bases of functions, they extend the estimators of densities to mixture models, deconvolution and inverse problems, to semi-parametric and nonparametric models for regressions, hazard functions and diffusions. They are estimated in the Hilbert spaces with respect to the distribution function of the regressors and their optimal rates of convergence are proved. Their mean square errors depend on the size of the basis which is consistently estimated by cross-validation. Wavelets estimators are defined and studied in the same models.The choice of the basis, with suitable parametrizations, and their estimation improve the existing methods and leads to applications to a wide class of models. The rates of convergence of the series estimators are the best among all nonparametric estimators with a great improvement in multidimensional models. Original methods are developed for the estimation in deconvolution and inverse problems. The asymptotic properties of test statistics based on the estimators are also established.
Автор: Bai Zhidong Название: Spectral Theory of Large Dimensional Random Matrices and its ISBN: 981457905X ISBN-13(EAN): 9789814579056 Издательство: World Scientific Publishing Цена: 12830.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book contains three parts: Spectral theory of large dimensional random matrices; Applications to wireless communications; and Applications to finance. In the first part, we introduce some basic theorems of spectral analysis of large dimensional random matrices that are obtained under finite moment conditions, such as the limiting spectral distributions of Wigner matrix and that of large dimensional sample covariance matrix, limits of extreme eigenvalues, and the central limit theorems for linear spectral statistics. In the second part, we introduce some basic examples of applications of random matrix theory to wireless communications and in the third part, we present some examples of Applications to statistical finance.
Описание: High-dimensional and nonparametric statistical models are ubiquitous in modern data science. This book develops a mathematically coherent and objective approach to statistical inference in such models, with a focus on function estimation problems arising from random samples (density estimation) or from Gaussian regression/signal in white noise problems.
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