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.
Описание: An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series.
Описание: There have been many advances in the theory and applications of discrete distributions in recent years. They can be applied to a wide range of problems, particularly in the health sciences, although a good understanding of their properties is very important. Discrete Distributions: Applications in the Health Sciences describes a number of new discrete distributions that arise in the statistical examination of real examples. For each example, an understanding of the issues surrounding the data provides the motivation for the subsequent development of the statistical models. Provides an overview of discrete distributions and their applications in the health sciences. Focuses on real examples, giving readers an insight into the utility of the models. Describes the properties of each distribution, and the methods that led to their development. Presents a range of examples from the health sciences, including cancer, epidemiology, and demography. Features discussion of software implementation -- in SAS, Fortran and R -- enabling readers to apply the methods to their own problems. Written in an accessible style, suitable for applied statisticians and numerate health scientists. Software and data sets are made available on the Web. Discrete Distributions: Applications in the Health Sciences provides a practical introduction to these powerful statistical tools and their applications, suitable for researchers and graduate students from statistics and biostatistics. The focus on applications, and the accessible style of the book, make it an excellent practical reference source for practitioners from the health sciences.
Описание: This book provides an accessible, serious, and multivariate introduction to the central limit theorem of random variables that lies at the heart of probability and statistics. Practical applications of stable random variables are showcased.
Описание: Dealing with 39 of the major probability distributions, the introductory chapters of this text introduce the fundamental concepts of the distributions and the relationships between variables. For each distribution that follows, the key formulae, tables and diagrams are presented.
Описание: Continuous Multivariate Distributions, Volume 1, Second Edition provides a remarkably comprehensive, self-contained resource for this critical statistical area. It covers all significant advances that have occurred in the field over the past quarter century in the theory, methodology, inferential procedures, computational and simulational aspects, and applications of continuous multivariate distributions. In-depth coverage includes MV systems of distributions, MV normal, MV exponential, MV extreme value, MV beta, MV gamma, MV logistic, MV Liouville, and MV Pareto distributions, as well as MV natural exponential families, which have grown immensely since the 1970s. Each distribution is presented in its own chapter along with descriptions of real-world applications gleaned from the current literature on continuous multivariate distributions and their applications.
Описание: This handbook brings together a comprehensive collection of mathematical material in one location. It also offers a variety of new results interpreted in a form that is particularly useful to engineers, scientists, and applied mathematicians.
Описание: 'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduate students in statistics and researchers in data-rich disciplines.
Описание: Random matrix theory has a long history, beginning in the first instance in multivariate statistics. It was used by Wigner to supply explanations for the important regularity features of the apparently random dispositions of the energy levels of heavy nuclei. This title contains chapters which serve as an introduction into this area of research.
Автор: N. Balakrishnan Название: A Primer on Statistical Distributions ISBN: 0471427985 ISBN-13(EAN): 9780471427988 Издательство: Wiley Рейтинг: Цена: 15824 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introducing the perfect, all-in-one primer on statistical distributions Statistical distributions, along with their properties and interrelationships, are a central part of advanced statistics. However, most statistics textbooks only devote a few chapters to basic statistical distributions such as binomial, Poisson, exponential, and normal and stop short of covering other important distributions geared toward upper-level statistics courses. That's where A Primer on Statistical Distributions makes its mark. Specifically tailored to the introductory course on statistical distributions, this unmatched resource takes a more balanced, all-inclusive approach than similar texts. In page after page, you'll find a valuable review of often-overlooked distributions, including geometric, negative binomial, hypergeometric, Pareto, beta, gamma, chi-square, logistic, Cauchy, Laplace, extreme value, multinomial, Dirichlet, and multivariate normal. A Primer on Statistical Distributions begins with an informative first chapter on preliminary notations, definitions, and the concepts that are necessary to work effectively with distributions. The basic topics covered in this intductory chapter include distribution types, generating functions, shape characteristics, entropy, random vectors, conditional distributions, and regressions. Subsequent chapters are divided into three parts: discrete distributions, continuous distributions, and multivariate distributions. Each chapter includes many skill-building exercises that provide a helpful review of the material just discussed. And the book also contains an appendix with engaging biographical sketches of some of the leading minds behind the development of statistical distributions theory. A Primer on Statistical Distributions is not only ideal for students and professionals in statistics, it can also benefit individuals in applied areas such as psychology, geography, economics, and engineering, and even professionals in need of a logically organized, comprehensive reference to statistical distributions. It all adds up to a text that no one utilizing statistical distributions should be without.
Описание: The Third Edition of the critically acclaimed "Univariate Discrete Distributions" addresses the latest advances in discrete distributions theory. New distributions, including q-series and generalized zeta-function distributions, are explored in detail. New families of distributions, including Lagrangian-type distributions, have been integrated into this thoroughly revised and updated text. In addition, new applications of univariate discrete distributions, including sports applications, have been added to demonstrate the flexibility of this powerful method. Throughout the Third Edition, extensive information has been added to reflect the new role of computer-based applications. With its thorough coverage and balanced presentation of theory and application, this is an excellent graduate-level textbook and essential reference for statisticians and mathematicians.
Описание: Featuring examples taken from the scientific literature, this book provides statisticians and researchers across the physical and social sciences with methods for fitting continuous probability distributions. It presents families with wide-ranging applicability, including Johnson`s system, kappa distribution, and generalized lambda distribution.
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