Описание: Decision making in all spheres of activity involves uncertainty. If rational decisions have to be made, they have to be based on the past observations of the phenomenon in question.
Автор: Simon French, David Rios Insua Название: Statistical Decision Theory: Kendall`s Library of Statistics 9 ISBN: 0470711051 ISBN-13(EAN): 9780470711057 Издательство: Wiley Рейтинг: Цена: 12189.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Decision-theoretic ideas can structure the process of inference together with the decision-making that inference supports. Statistical decision theory is the sub-discipline of statistics which explores and develops this structure.
Автор: Lunn, David, Название: The BUGS Book ISBN: 1584888490 ISBN-13(EAN): 9781584888499 Издательство: Taylor&Francis Рейтинг: Цена: 7042.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Bayesian methods have become the widely used statistical methods for data analysis and modeling. The BUGS software has become the popular software for Bayesian analysis worldwide. This title provides a practical introduction to this program and its use. It covers the functionalities of BUGS, including prediction, missing data, and model criticism.
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: Поставка под заказ.
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: Inference Framework and Method.- Measurement Error and Misclassification: Introduction.- Survival Data with Measurement Error.- Recurrent Event Data with Measurement Error.- Longitudinal Data with Covariate Measurement Error.- Multi-State Models with Error-Prone Data.- Case-Control Studies with Measurement Error or Misclassification.- Analysis with Error in Responses.- Miscellaneous Topics.- Appendix.- References.
Описание: Addresses statistical challenges posed by inaccurately measuring explanatory variables, a common problem in biostatistics and epidemiology. This book explores both measurement error in continuous variables and misclassification in categorical variables. It is suitable for biostatisticians, epidemiologists, and students.
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