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On Model Uncertainty and its Statistical Implications, Theo K. Dijkstra


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Автор: Theo K. Dijkstra
Название:  On Model Uncertainty and its Statistical Implications
ISBN: 9783540193678
Издательство: Springer
Классификация:
ISBN-10: 3540193677
Обложка/Формат: Paperback
Страницы: 138
Вес: 0.22 кг.
Дата издания: 25.05.1988
Серия: Lecture Notes in Economics and Mathematical Systems
Язык: English
Размер: 234 x 156 x 8
Основная тема: Mathematics
Подзаголовок: Proceedings of a Workshop, Held in Groningen, The Netherlands, September 25–26, 1986
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model.


The Elements of Statistical Learning

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 11528.00 р.
Наличие на складе: Заказано в издательстве.

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.

Statistical Methods for Recommender Systems

Автор: Agarwal
Название: Statistical Methods for Recommender Systems
ISBN: 1107036070 ISBN-13(EAN): 9781107036079
Издательство: Cambridge Academ
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Цена: 7920.00 р.
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Описание: Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

Applied Linear Statistical Models with Student CD

Автор: Nachtsheim;Neter;Kutner
Название: Applied Linear Statistical Models with Student CD
ISBN: 0071122214 ISBN-13(EAN): 9780071122214
Издательство: McGraw-Hill
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Цена: 9265.00 р.
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Описание: "Applied Linear Statistical Models", 5e, is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.

A Statistical Model

Автор: Stephen E. Fienberg; Cleo Youtz; David C. Hoaglin;
Название: A Statistical Model
ISBN: 1461279925 ISBN-13(EAN): 9781461279921
Издательство: Springer
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Цена: 16769.00 р.
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Описание: A large number of Mostellar`s friends, colleagues, collaborators, and former students have contributed to the preparation of this volume in honor of his 70th birthday. It provides a critical assessment of Mosteller`s professional and research contributions to the field of statistics and its applications.

Statistical models and methods for financial markets

Автор: Lai, Tze Leung Xing, Haipeng
Название: Statistical models and methods for financial markets
ISBN: 1441926682 ISBN-13(EAN): 9781441926685
Издательство: Springer
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Цена: 10335.00 р.
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Описание: The authors here present statistical methods and models of importance to quantitative finance and links finance theory to market practice via statistical modeling and decision making. They provide basic statistical background as well as in-depth applications.

Statistical and Machine-Learning Data Mining

Автор: Ratner Bruce
Название: Statistical and Machine-Learning Data Mining
ISBN: 1439860912 ISBN-13(EAN): 9781439860915
Издательство: Taylor&Francis
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Цена: 9033.00 р.
Наличие на складе: Нет в наличии.

Описание: Rev. ed. of: Statistical modeling and analysis for database marketing. c2003.

Empirico-Statistical Analysis of Narrative Material and its Applications to Historical Dating

Автор: A.T. Fomenko
Название: Empirico-Statistical Analysis of Narrative Material and its Applications to Historical Dating
ISBN: 0792326059 ISBN-13(EAN): 9780792326052
Издательство: Springer
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Цена: 16764.00 р.
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Описание: Presents a comprehensive set of mathematical and statistical techniques for the analysis of chronological data. This work concentrates on the development of the mathematical statistical tools and their application to astronomical data, including the Almagest and simulated data (to test the validity of the methods).

Empirico-Statistical Analysis of Narrative Material and its Applications to Historical Dating

Автор: A.T. Fomenko
Название: Empirico-Statistical Analysis of Narrative Material and its Applications to Historical Dating
ISBN: 0792326040 ISBN-13(EAN): 9780792326045
Издательство: Springer
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Цена: 22354.00 р.
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Описание: Today the methods of applied statistics have penetrated very different fields of knowledge, including the investigation oftexts ofvarious origins. These "texts" may be considered as signal sequences of different kinds, long genetic codes, graphic representations (which may be coded and represented by a "text"), as well as actual narrative texts (for example, historical chronicles, originals, documents, etc. ). One ofthe most important problems arising here is to recognize dependent text, i. e., texts which have a measure of "resemblance", arising from some kind of "common origin". For instance, in pattern-recognition problems, it is essential to identify from a large set of "patterns" a pattern that is "closest" to a given one; in studying long signal sequences, it is important to recognize "homogeneous subsequences" and the places of their junction. This includes, in particular, the well-known change-point prob lern, which is given considerable attention in mathematical statistics and the theory of stochastic processes. As applied to the study of narrative texts, the problern of recognizing depen- dent and independent texts ( e . g., chronicles) Ieads to the problern offinding texts having a common source, i. e., the sameoriginal (such texts are naturally called dependent), or, on the contrary, having different sources (such texts are natu- rally called independent). Clearly, such problems are exceedingly complicated, and therefore the appearance of new empirico-statistical recognition methods which, along with the classical approaches, may prove useful in concrete studies (e. g., source determination) is welcome.

The BUGS Book

Автор: Lunn, David,
Название: The BUGS Book
ISBN: 1584888490 ISBN-13(EAN): 9781584888499
Издательство: Taylor&Francis
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Цена: 7042.00 р.
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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.

Statistical Learning for Biomedical Data

Автор: Malley
Название: Statistical Learning for Biomedical Data
ISBN: 0521699096 ISBN-13(EAN): 9780521699099
Издательство: Cambridge Academ
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Цена: 6494.00 р.
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Описание: Biomedical researchers need machine learning techniques to make predictions such as survival/death or response to treatment when data sets are large and complex. This highly motivating introduction to these machines explains underlying principles in nontechnical language, using many examples and figures, and connects these new methods to familiar techniques.

An Introduction to Multivariate Statistical Analysis, Third Edition

Автор: T. W. Anderson
Название: An Introduction to Multivariate Statistical Analysis, Third Edition
ISBN: 0471360910 ISBN-13(EAN): 9780471360919
Издательство: Wiley
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Цена: 27712.00 р.
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Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.

Research design and statistical analysis

Автор: Myers, Jerome L
Название: Research design and statistical analysis
ISBN: 0805864318 ISBN-13(EAN): 9780805864311
Издательство: Taylor&Francis
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Цена: 22202.00 р.
Наличие на складе: Нет в наличии.

Описание: This interdisciplinary group of scholars-anthropologists, archaeologists, architects, educators, lawyers, heritage administrators, policy analysts, and consultants-make the first attempt to define and assess heritage values on a local, national and global level. Chapters range from the theoretical to policy frameworks to case studies of heritage practice, written by scholars from eight countries.


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