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Methods for estimation and inference in modern econometrics, Anatolyev, Stanislav Gospodinov, Nikolay

Methods for estimation and inference in modern econometrics

Автор: Anatolyev, Stanislav Gospodinov, Nikolay
Название:  Methods for estimation and inference in modern econometrics   (Методы для оценки и вывода в современной эконометрике)
Издательство: Taylor&Francis
Вероятность и статистика

ISBN: 1439838240
ISBN-13(EAN): 9781439838242
ISBN: 1-439-83824-0
ISBN-13(EAN): 978-1-439-83824-2
Обложка/Формат: Hardback
Страницы: 234
Вес: 0.468 кг.
Дата издания: 06.06.2011
Иллюстрации: 50 black & white illustrations
Размер: 243 x 152 x 18
Читательская аудитория: Postgraduate, research & scholarly
Поставляется из: Англии

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Maximum likelihood estimation and inference

Автор: Millar, Russell
Название: Maximum likelihood estimation and inference
ISBN: 0470094826 ISBN-13(EAN): 9780470094822
Издательство: Wiley
Цена: 6600 р.
Наличие на складе: Нет в наличии.

Bayesian Inference / Parameter Estimation and Decisions

Автор: Harney Hanns L.
Название: Bayesian Inference / Parameter Estimation and Decisions
ISBN: 3540003975 ISBN-13(EAN): 9783540003977
Издательство: Springer
Цена: 7475 р.
Наличие на складе: Нет в наличии.
Описание: Filling a longstanding need in the physical sciences, Bayesian Inference offers the first basic introduction for advanced undergraduates and graduates in the physical sciences. This text and reference generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This usually occurs in frontier science because the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins. In this case, the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. Requiring no knowledge of quantum mechanics, the text is written on introductory level, with many examples and exercises, for physicists planning to, or working in, fields such as medical physics, nuclear physics, quantum mechanics, and chaos. 

Estimation of Dependences Based on Empirical Data / Empirical Inference Science

Автор: Vapnik Vladimir, Kotz S.
Название: Estimation of Dependences Based on Empirical Data / Empirical Inference Science
ISBN: 0387308652 ISBN-13(EAN): 9780387308654
Издательство: Springer
Цена: 6156 р.
Наличие на складе: Нет в наличии.
Описание: In 1982, Springer published the English translation of the Russian book Estimation of Dependencies Based on Empirical Data which became the foundation of the statistical theory of learning and generalization (the VC theory). A number of new principles and new technologies of learning, including SVM technology, have been developed based on this theory.The second edition of this book contains two parts:- A reprint of the first edition which provides the classical foundation of Statistical Learning Theory- Four new chapters describing the latest ideas in the development of statistical inference methods. They form the second part of the book entitled Empirical Inference ScienceThe second part of the book discusses along with new models of inference the general philosophical principles of making inferences from observations. It includes new paradigms of inference that use non-inductive methods appropriate for a complex world, in contrast to inductive methods of inference developed in the classical philosophy of science for a simple world.The two parts of the book cover a wide spectrum of ideas related to the essence of intelligence: from the rigorous statistical foundation of learning models to broad philosophical imperatives for generalization.The book is intended for researchers who deal with a variety of problems in empirical inference: statisticians, mathematicians, physicists, computer scientists, and philosophers.

Combinatorial Methods in Density Estimation

Автор: Devroye Luc, Lugosi Gabor
Название: Combinatorial Methods in Density Estimation
ISBN: 0387951172 ISBN-13(EAN): 9780387951171
Издательство: Springer
Цена: 6595 р.
Наличие на складе: Нет в наличии.
Описание: Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem of the selection of the bin widths. This text explores a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all density estimates and for most model selection problems, both parametric and nonparametric. It is the first book on this topic. The text is intended for first-year graduate students in statistics and learning theory, and offers a host of opportunities for further research and thesis topics. Each chapter corresponds roughly to one lecture, and is supplemented with many classroom exercises. A one year course in probability theory at the level of Feller's Volume 1 should be more than adequate preparation. Gabor Lugosi is Professor at Universitat Pompeu Fabra in Barcelona, and Luc Debroye is Professor at McGill University in Montreal. In 1996, the authors, together with LГЎszlo GyГ¶rfi, published the successful text, A Probabilistic Theory of Pattern Recognition with Springer-Verlag. Both authors have made many contributions in the area of nonparametric estimation.

Missing Data and Small-Area Estimation / Modern Analytical Equipment for the Survey Statistician

Автор: Longford Nicholas T.
Название: Missing Data and Small-Area Estimation / Modern Analytical Equipment for the Survey Statistician
ISBN: 1852337605 ISBN-13(EAN): 9781852337605
Издательство: Springer
Цена: 10559 р.
Наличие на складе: Нет в наличии.
Описание: This book develops methods for two key problems in the analysis of large-scale surveys: dealing with incomplete data and making inferences about sparsely represented subdomains. The presentation is committed to two particular methods, multiple imputation for missing data and multivariate composition for small-area estimation. The methods are presented as developments of established approaches by attending to their deficiencies. Thus the change to more efficient methods can be gradual, sensitive to the management priorities in large research organisations and multidisciplinary teams and to other reasons for inertia. The typical setting of each problem is addressed first, and then the constituency of the applications is widened to reinforce the view that the general method is essential for modern survey analysis. The general tone of the book is not "from theory to practice," but "from current practice to better practice." The third part of the book, a single chapter, presents a method for efficient estimation under model uncertainty. It is inspired by the solution for small-area estimation and is an example of "from good practice to better theory." A strength of the presentation is chapters of case studies, one for each problem. Whenever possible, turning to examples and illustrations is preferred to the theoretical argument. The book is suitable for graduate students and researchers who are acquainted with the fundamentals of sampling theory and have a good grounding in statistical computing, or in conjunction with an intensive period of learning and establishing one's own a modern computing and graphical environment that would serve the reader for most of the analytical work in the future.While some analysts might regard data imperfections and deficiencies, such as nonresponse and limited sample size, as someone else's failure that bars effective and valid analysis, this book presents them as respectable analytical and inferential challenges, opportunities to harness the computing power into service of high-quality socially relevant statistics. Overriding in this approach is the general principle—to do the best, for the consumer of statistical information, that can be done with what is available. The reputation that government statistics is a rigid procedure-based and operation-centred activity, distant from the mainstream of statistical theory and practice, is refuted most resolutely. From the reviews:"Ultimately, this book serves as an excellent reference source to guide and improve statistical practice in survey settings exhibiting these problems." Psychometrika"I am convinced this book will be useful to practitioners...[and a] valuable resource for future research in this field." Jan Kordos in Statistics in Transition, Vol. 7, No. 5, June 2006

Modern statistical and mathematical methods in reliability

Название: Modern statistical and mathematical methods in reliability
ISBN: 9812563563 ISBN-13(EAN): 9789812563569
Издательство: World Scientific Publishing
Цена: 9587 р.
Наличие на складе: Нет в наличии.
Описание: This volume contains extended versions of 28 carefully selected and reviewed papers presented at The Fourth International Conference on Mathematical Methods in Reliability in Santa Fe, New Mexico, June 21-25, 2004, the leading conference in reliability research. The meeting serves as a forum for discussing fundamental issues on mathematical methods in reliability theory and its applications. A broad overview of current research activities in reliability theory and its applications is provided with coverage on reliability modelling, network and system reliability, Bayesian methods, survival analysis, degradation and maintenance modelling, and software reliability.

The contributors are all leading experts in the field and include the plenary session speakers, Tim Bedford, Thierry Duchesne, Henry Wynn, Vicki Bier, Edsel Pena, Michael Hamada, and Todd Graves.


Fundamentals of Modern Statistical Methods / Substantially Improving Power and Accuracy

Автор: Wilcox Rand R.
Название: Fundamentals of Modern Statistical Methods / Substantially Improving Power and Accuracy
ISBN: 0387951571 ISBN-13(EAN): 9780387951577
Издательство: Springer
Цена: 5276 р.
Наличие на складе: Нет в наличии.
Описание: Conventional statistical methods have a very serious flaw. They routinely miss differences among groups or associations among variables that are detected by more modern techniques - even under very small departures from normality. Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable. Improved methods have been derived, but they are far from obvious or intuitive based on the training most researchers receive. Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods. Without assuming any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included to illustrate the practical problems with conventional procedures and how more modern methods can make a substantial difference in the conclusions reached in many areas of statistical research.Rand Wilcox is a professor of psychology at the University of Southern California. He is a fellow of the Royal Statistical Society and the American Psychological Society. Dr. Wilcox currently serves as an associate editor of Computational Statistics & Data Analysis and Psychometrika. He has published over 165 articles in a wide range of statistical journals and he is the author of three other books on statistics.|ВїThe volume is a jewel of direct explanations and information necessary for a good understanding of analysis of data, aimed at ordinary researchers who must try to present reasonable interpretable accounts of their data or judge when to abandon a particular strategy..."|- Perceptual and Motor Skills, 2002

Modern regression methods

Автор: Ryan, Thomas P.
Название: Modern regression methods
ISBN: 0470081864 ISBN-13(EAN): 9780470081860
Издательство: Wiley
Цена: 9185 р.
Наличие на складе: Нет в наличии.
Описание: "Over the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one. " (The American Statistician) Regression is a popular research area, and regression analysis is an ever-changing collection of techniques.

Modern Statistical Methods for Astronomy

Автор: Feigelson Eric D
Название: Modern Statistical Methods for Astronomy
ISBN: 052176727X ISBN-13(EAN): 9780521767279
Издательство: Cambridge Academ
Цена: 7120 р.
Наличие на складе: Нет в наличии.
Описание: Linking astronomy to the world of modern statistics, this book is a unique resource introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. Examining fields of statistics applicable to astronomy, this is invaluable for graduate students and researchers facing complex data analysis tasks.

Modern Imputation Methods in Practice

Автор: Nathaniel Schenker, Donald B. Rubin, Roderick J. L
Название: Modern Imputation Methods in Practice
ISBN: 1584881011 ISBN-13(EAN): 9781584881018
Издательство: Taylor&Francis
Цена: 3189 р.
Наличие на складе: Нет в наличии.

The Refinement of Econometric Estimation and Test Procedures

Автор: Edited by Garry D. A. Phillips
Название: The Refinement of Econometric Estimation and Test Procedures
ISBN: 0521870534 ISBN-13(EAN): 9780521870535
Издательство: Cambridge Academ
Цена: 6574 р.
Наличие на складе: Нет в наличии.
Описание: The small sample properties of estimators and tests are frequently too complex to be useful or are unknown. Much econometric theory is therefore developed for very large or asymptotic samples where it is assumed that the behaviour of estimators and tests will adequately represent their properties in small samples. Refined asymptotic methods adopt an intermediate position by providing improved approximations to small sample behaviour using asymptotic expansions. Dedicated to the memory of Michael Magdalinos, whose work is a major contribution to this area, this book contains chapters directly concerned with refined asymptotic methods. In addition, there are chapters focussing on new asymptotic results; the exploration through simulation of the small sample behaviour of estimators and tests in panel data models; and improvements in methodology. With contributions from leading econometricians, this collection will be essential reading for researchers and graduate students concerned with the use of asymptotic methods in econometric analysis.

Theory of Point Estimation

Автор: Lehmann E.L., Casella George
Название: Theory of Point Estimation
ISBN: 0387985026 ISBN-13(EAN): 9780387985022
Издательство: Springer
Цена: 7036 р.
Наличие на складе: Нет в наличии.
Описание: This second, much enlarged edition by Lehmann and Casella of Lehmann's classic text on point estimation maintains the outlook and general style of the first edition. All of the topics are updated. An entirely new chapter on Bayesian and hierarchical Bayesian approaches is provided, and there is much new material on simultaneous estimation. Each chapter concludes with a Notes section which contains suggestions for further study. The book is a companion volume to the second edition of Lehmann's "Testing Statistical Hypotheses".E.L. Lehmann is Professor Emeritus at the University of California, Berkeley. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and the recipient of honorary degrees from the University of Leiden, The Netherlands, and the University of Chicago.George Casella is the Liberty Hyde Bailey Professor of Biological Statistics in The College of Agriculture and Life Sciences at Cornell University. Casella has served as associate editor of The American Statistician, Statistical Science and JASA. He is currently the Theory and Methods Editor of JASA. Casella has authored two other textbooks (Statistical Inference, 1990, with Roger Berger and Variance Components, 1992, with Shayle A. Searle and Charles McCulloch). He is a fellow of the IMS and ASA, and an elected fellow of the ISI.Also available:E.L. Lehmann, Testing Statistical Hypotheses Second Edition, Springer-Verlag New York, Inc., ISBN 0-387-949194.

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