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Author:
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 910
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Book Description
Author:
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 910
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Book Description
Author: P.K. Bhattacharya
Publisher: Academic Press
ISBN: 0128041234
Category : Mathematics
Languages : en
Pages : 544
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Book Description
Theory and Methods of Statistics covers essential topics for advanced graduate students and professional research statisticians. This comprehensive resource covers many important areas in one manageable volume, including core subjects such as probability theory, mathematical statistics, and linear models, and various special topics, including nonparametrics, curve estimation, multivariate analysis, time series, and resampling. The book presents subjects such as "maximum likelihood and sufficiency," and is written with an intuitive, heuristic approach to build reader comprehension. It also includes many probability inequalities that are not only useful in the context of this text, but also as a resource for investigating convergence of statistical procedures. Codifies foundational information in many core areas of statistics into a comprehensive and definitive resource Serves as an excellent text for select master’s and PhD programs, as well as a professional reference Integrates numerous examples to illustrate advanced concepts Includes many probability inequalities useful for investigating convergence of statistical procedures
Author: D.V. Hinkley
Publisher: Chapman and Hall/CRC
ISBN:
Category : Mathematics
Languages : en
Pages : 386
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Book Description
Statistical Theory and Modelling is a celebration of the work of Sir David Cox, FRS, and reflects his many interests in statistical theory and methods. It is a series of review articles, intended as an introduction to a variety of topics suitable for the graduate student and practicing statistician. Many of the topics are the subject of book-length treatments by Sir David and authors of this volume. Each chapter leads to a larger literature. Topics range the breadth of statistics and include modern degvelopments in statistical theory and methods. Special topics covered are generalized linear models, residuals and diagnostics, survival analysis, sequential analysis, time series, stochastic modelling of spatial data, design of experiments, likelihood inference and statistical approximation.
Author:
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 750
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Book Description
Author: K. A. Brownlee
Publisher:
ISBN:
Category :
Languages : en
Pages : 590
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Book Description
Author: Felix Abramovich
Publisher: CRC Press
ISBN: 148221184X
Category : Mathematics
Languages : en
Pages : 240
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Book Description
Designed for a one-semester advanced undergraduate or graduate course, Statistical Theory: A Concise Introduction clearly explains the underlying ideas and principles of major statistical concepts, including parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, Bayesian inference, and elements of decision theory. It i
Author:
Publisher: John Wiley & Sons
ISBN: 0471744069
Category : Mathematics
Languages : en
Pages : 562
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Book Description
ENCYCLOPEDIA OF STATISTICAL SCIENCES
Author: United States. Social Security Administration. Office of Research and Statistics
Publisher:
ISBN:
Category : Bibliographical literature
Languages : en
Pages : 612
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Book Description
Over 2500 references about social security. Classified order. Author, subject indexes.
Author:
Publisher:
ISBN:
Category : Public welfare
Languages : en
Pages : 612
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Book Description
Author: Vladimir Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475732643
Category : Mathematics
Languages : en
Pages : 324
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Book Description
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
