The Statistical Analysis of Series of Events, David R. Cox
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: McElreath, Richard Название: Statistical Rethinking ISBN: 036713991X ISBN-13(EAN): 9780367139919 Издательство: Taylor&Francis Рейтинг: Цена: 83690.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in statistical modeling. Pushes readers to perform step-by-step calculations (usually automated.) Unique, computational approach.
Автор: Donald B. Percival, Andrew T. Walden Название: Spectral Analysis for Univariate Time Series ISBN: 1107028140 ISBN-13(EAN): 9781107028142 Издательство: Cambridge Academ Рейтинг: Цена: 97150.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.
Автор: Richard J. Cook; Jerald Lawless Название: The Statistical Analysis of Recurrent Events ISBN: 1441924159 ISBN-13(EAN): 9781441924155 Издательство: Springer Рейтинг: Цена: 135090.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents models and statistical methods for the analysis of recurrent event data. More general intensity-based models are also considered, as well as simpler models that focus on rate or mean functions.
Автор: Boris Ryabko; Jaakko Astola; Mikhail Malyutov Название: Compression-Based Methods of Statistical Analysis and Prediction of Time Series ISBN: 3319322516 ISBN-13(EAN): 9783319322513 Издательство: Springer Рейтинг: Цена: 71730.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In the meantime, it was realized that they can be used for solving important problems of prediction and statistical analysis of time series, and this book describes recent results in this area.The first chapter introduces and describes the application of universal codes to prediction and the statistical analysis of time series;
Автор: Vershynin, Roman (university Of Michigan, Ann Arbor) Название: Cambridge series in statistical and probabilistic mathematics ISBN: 1108415199 ISBN-13(EAN): 9781108415194 Издательство: Cambridge Academ Рейтинг: Цена: 60190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The data sciences are moving fast, and probabilistic methods are both the foundation and a driver. This highly motivated text brings beginners up to speed quickly and provides working data scientists with powerful new tools. Ideal for a basic second course in probability with a view to data science applications, it is also suitable for self-study.
Автор: Wainwright Martin J Название: Cambridge Series in Statistical and Probabilistic Mathematic ISBN: 1108498027 ISBN-13(EAN): 9781108498029 Издательство: Cambridge Academ Рейтинг: Цена: 71810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Recent years have seen an explosion in the volume and variety of data collected in scientific disciplines from astronomy to genetics and industrial settings ranging from Amazon to Uber. This graduate text equips readers in statistics, machine learning, and related fields to understand, apply, and adapt modern methods suited to large-scale data.
Автор: Bouveyron, Charles Celeux, Gilles Murphy, T. Brendan (university College Dublin) Raftery, Adrian E. (university Of Washington) Название: Cambridge series in statistical and probabilistic mathematics ISBN: 110849420X ISBN-13(EAN): 9781108494205 Издательство: Cambridge Academ Рейтинг: Цена: 77090.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods.
Автор: Daniil Ryabko Название: Asymptotic Nonparametric Statistical Analysis of Stationary Time Series ISBN: 3030125637 ISBN-13(EAN): 9783030125639 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Stationarity is a very general, qualitative assumption, that can be assessed on the basis of application specifics. It is thus a rather attractive assumption to base statistical analysis on, especially for problems for which less general qualitative assumptions, such as independence or finite memory, clearly fail. However, it has long been considered too general to be able to make statistical inference. One of the reasons for this is that rates of convergence, even of frequencies to the mean, are not available under this assumption alone. Recently, it has been shown that, while some natural and simple problems, such as homogeneity, are indeed provably impossible to solve if one only assumes that the data is stationary (or stationary ergodic), many others can be solved with rather simple and intuitive algorithms. The latter include clustering and change point estimation among others. In this volume I summarize these results. The emphasis is on asymptotic consistency, since this the strongest property one can obtain assuming stationarity alone. While for most of the problem for which a solution is found this solution is algorithmically realizable, the main objective in this area of research, the objective which is only partially attained, is to understand what is possible and what is not possible to do for stationary time series. The considered problems include homogeneity testing (the so-called two sample problem), clustering with respect to distribution, clustering with respect to independence, change point estimation, identity testing, and the general problem of composite hypotheses testing. For the latter problem, a topological criterion for the existence of a consistent test is presented. In addition, a number of open problems is presented.
Автор: Ryabko Boris, Astola Jaakko, Malyutov Mikhail Название: Compression-Based Methods of Statistical Analysis and Prediction of Time Series ISBN: 3319812343 ISBN-13(EAN): 9783319812342 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In the meantime, it was realized that they can be used for solving important problems of prediction and statistical analysis of time series, and this book describes recent results in this area. The first chapter introduces and describes the application of universal codes to prediction and the statistical analysis of time series;
Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.
New to the Second Edition
The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
New chapter on simulation that includes examples of data generated from complex models and distributions
A detailed discussion of the philosophy and use of the knitr and markdown packages for R
New packages that extend the functionality of R and facilitate sophisticated analyses
Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots
Easily Find Your Desired Task
Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.
Автор: Robert Nisbet , Gary Miner, Ken Yale Название: Handbook of Statistical Analysis and Data Mining Applications, 2 ed. ISBN: 0124166326 ISBN-13(EAN): 9780124166325 Издательство: Elsevier Science Рейтинг: Цена: 88690.00 T Наличие на складе: Поставка под заказ. Описание:
Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.
This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas--from science and engineering, to medicine, academia and commerce.
Includes input by practitioners for practitioners
Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models
Contains practical advice from successful real-world implementations
Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions
Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
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