Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. 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: James, Gareth Witten, Daniela Hastie, Trevor Tibsh Название: Introduction to statistical learning ISBN: 1071614177 ISBN-13(EAN): 9781071614174 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.
Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.
An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
Автор: Durbin, James; Koopman, Siem Jan Название: Time Series Analysis by State Space Methods ISBN: 019964117X ISBN-13(EAN): 9780199641178 Издательство: Oxford Academ Рейтинг: Цена: 18216.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This new edition updates Durbin & Koopman`s important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.
Автор: Bradley Efron , Trevor Hastie Название: Computer Age Statistical Inference, Student Edition ISBN: 1108823416 ISBN-13(EAN): 9781108823418 Издательство: Cambridge Academ Рейтинг: Цена: 5069.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.
Автор: Christian Robert; George Casella Название: Monte Carlo Statistical Methods ISBN: 1441919392 ISBN-13(EAN): 9781441919397 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.
Название: Real-World Evidence in Drug Development and Evaluation ISBN: 036702621X ISBN-13(EAN): 9780367026219 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book concerns use of real world data (RWD) and real world evidence (RWE) to aid drug development across product cycle. RWD are healthcare data that are collected outside the constraints of conventual controlled randomized trials (CRTs); whereas RWE is the knowledge derived from aggregation and analysis of RWD.
Описание: Before the data revolution, most books focused either on mathematical modeling of chemical processes or exploratory chemometrics. This book aims to combine these two approaches and provide aspiring chemical engineers a single, comprehensive account of computational and statistical methods. Each chapter is accompanied by extensive exercises.
Автор: Frederick Jelinek Название: Statistical Methods for Speech Recognition ISBN: 0262546604 ISBN-13(EAN): 9780262546607 Издательство: MIT Press Рейтинг: Цена: 14944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Rhys Jones Название: Statistical Literacy A Beginner`s Guide ISBN: 1529754801 ISBN-13(EAN): 9781529754803 Издательство: Sage Publications Рейтинг: Цена: 15206.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
In an increasingly data-centric world, we all need to know how to read and interpret statistics. But where do we begin?
This book breaks statistical terms and concepts down in a clear, straightforward way. From understanding what data are telling you to exploring the value of good storytelling with numbers, it equips you with the information and skills you need to become statistically literate.
It also:
Dispels misconceptions about the nature of statistics to help you avoid common traps.
Helps you put your learning into practice with over 60 Tasks and Develop Your Skills activities.
Draws on real-world research to demonstrate the messiness of data – and show you a path through it.
Approachable and down to earth, this guide is aimed at undergraduates across the social sciences, psychology, business and beyond who want to engage confidently with quantitative methods or statistics. It forms a reassuring aid for anyone looking to understand the foundations of statistics before their course advances, or as a refresher on key content.
Автор: Gelfand, Alan E. Fuentes, Montserrat Guttorp, Pete Название: Handbook of spatial statistics ISBN: 1420072870 ISBN-13(EAN): 9781420072877 Издательство: Taylor&Francis Рейтинг: Цена: 15541.00 р. 22202.00-30% Наличие на складе: Есть (2 шт.) Описание: Offers an introduction detailing the evolution of the field of spatial statistics. This title focuses on the three main branches of spatial statistics: continuous spatial variation (point referenced data); discrete spatial variation, including lattice and areal unit data; and, spatial point patterns.
Автор: Giorgio Luciano (Editor) Название: Statistical And Multivariate Analys ISBN: 1138196304 ISBN-13(EAN): 9781138196308 Издательство: Taylor&Francis Рейтинг: Цена: 16078.00 р. 22968.00-30% Наличие на складе: Есть (1 шт.) Описание: The present work is an introductory text in statistics, addressed to researchers and students in the field of material science. It aims to give the readers basic knowledge on how statistical reasoning is exploitable in this field, improving their knowledge of statistical tools.
Автор: Fan, Jianqing Название: Statistical Learning For High-Dimen ISBN: 1466510846 ISBN-13(EAN): 9781466510845 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable reference for researchers involved with model selection, variable selection, machine learning, and risk management.
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