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A History of Parametric Statistical Inference from Bernoulli to Fisher, 1713-1935, Hald Anders



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Автор: Hald Anders
Название:  A History of Parametric Statistical Inference from Bernoulli to Fisher, 1713-1935
Перевод названия: Андерс Хальд: История заключений параметрической статистики от Бернулли до Фишера, 1713-1930 гг.
ISBN: 9780387464084
Издательство: Springer
Классификация:
ISBN-10: 0387464085
Обложка/Формат: Hardback
Страницы: 240
Вес: 0.496 кг.
Дата издания: 29.12.2006
Серия: Sources and Studies in the History of Mathematics and Physical Sciences
Язык: English
Иллюстрации: 11 black & white illustrations, 1 black & white ta
Размер: 166 x 239 x 20
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Германии
Дополнительное описание: Формат: 235x155
Илюстрации: 11
Круг читателей: Students, researchers
Ключевые слова:
Язык: eng
Оглавление: Introduction.- The Three Revolutions in Parametric Statistical Inference.- James Bernoulli’s Law of Large Numbers for the Binomial, 1713, and its Generalization.- De Moivre's Normal Approximation to the Binomial, 1733, and its Generalizations.- Bayes's Posterior Distribution of the Binomial Parameter and His Rule for Inductive Inference, 1764.- Laplace’s Theory of Inverse Probability, 1774-1786.- A Nonprobabilistic Interlude: The Fitting of Equations to Data, 1750-1805.- Gauss’s Derivation of the Normal Distribution and the Method of Least Squares, 1809.- Credibility and Confidence Intervals by Laplace and Gauss.- The Multivariate Posterior Distribution.- Edgeworth’s Genuine Inverse Method and the Equivalence of Inverse and Direct Probability in Large Samples, 1908 and 1909.- Criticisms of Inverse Probability.- Laplace’s Central Limit Theorem and Linear Minimum Variance Estimation.- Gauss’s Theory of Linear Minimum Variance Estimation.- The Development of a Frequentist Error Theory.- Skew Distributions and the Method of Moments.- Normal Correlation and Regression.- Sampling Distributions Under Normality, 1876-1908.- Fisher's Early papers, 1912-1921.- The revolutionary paper, 1922.- Studentization, the F Distribution and the Analysis of Variance, 1922-1925.- The Likelihood Function, Ancillarity and Conditional Inference .- References.- Subject Index.- Author Index.





Parametric and Nonparametric Inference from Record-Breaking Data

Автор: Gulati Sneh, Padgett William J.
Название: Parametric and Nonparametric Inference from Record-Breaking Data
ISBN: 0387001387 ISBN-13(EAN): 9780387001388
Издательство: Springer
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Цена: 11879 р.
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Описание: This book provides a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, including Bayesian inference. A unique feature is that it treats the area of nonparametric function estimation from such data in detail, gathering results on this topic to date in one accessible volume. Previous books on records have focused mainly on the probabilistic behavior of records, prediction of future records, and characterizations of the distributions of record values, addressing some inference methods only briefly. The main purpose of this book is to fill this void on general inference from record values.Statisticians, mathematicians, and engineers will find the book useful as a research reference and in learning about making inferences from record-breaking data. The book can also serve as part of a graduate-level statistics or mathematics course, complementing material on the probabilistic aspects of record values. For a basic understanding of the statistical concepts, a one-year graduate course in mathematical statistics provides sufficient background. For a detailed understanding of the convergence theory of the nonparametric function estimators, a course in measure theory or probability theory at the graduate level is useful. Sneh Gulati is Associate Professor of Statistics at Florida International University in Miami. She is currently an associate editor of the Journal of Statistical Computation and Simulation and has published several articles in statistics. Currently she serves as the president of the South Florida Chapter of the American Statistical Association and is also the chair of the Florida Commission of Hurricane Loss Projection Methodology.William J. Padgett is Professor of Statistics and was the founding Chair of the Department of Statistics at the University of South Carolina, Columbia. He has published numerous papers and articles, as well as three books, on statistics and probability and has served as an associate editor of eight statistical journals, including Technometrics, Lifetime Data Analysis, Naval Research Logistics, Journal of Statistical Computation and Simulation, and the Journal of Statistical Planning and Inference. He is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics and an elected ordinary member of the International Statistical Institute.

Causal Inference for Statistics, Social, and Biomedical Sciences

Автор: Imbens
Название: Causal Inference for Statistics, Social, and Biomedical Sciences
ISBN: 0521885884 ISBN-13(EAN): 9780521885881
Издательство: Cambridge Academ
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Цена: 8495 р.
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Описание: Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

Bayesian inference in statistical analysis

Автор: Box, George E. P. Tiao, George C.
Название: Bayesian inference in statistical analysis
ISBN: 0471574287 ISBN-13(EAN): 9780471574286
Издательство: Wiley
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Цена: 27770 р.
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Описание: Designed to form the basis of a graduate course on Bayesian inference, this textbook discusses important general issues of the Bayesian approach. It investigates problems, illustrating the appropriate analysis of mathematical results with numerical examples.

Concise Introduction to Statistical Inference

Автор: Thijssen Jacco
Название: Concise Introduction to Statistical Inference
ISBN: 1498755771 ISBN-13(EAN): 9781498755771
Издательство: Taylor&Francis
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Цена: 9618 р.
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Описание: This short book introduces the main ideas of statistical inference in a way that is both user friendly and mathematically sound. Particular emphasis is placed on the common foundation of many models used in practice. In addition, the book focuses on the formulation of appropriate statistical models to study problems in business, economics, and the social sciences, as well as on how to interpret the results from statistical analyses. The book will be useful to students who are interested in rigorous applications of statistics to problems in business, economics and the social sciences, as well as students who have studied statistics in the past, but need a more solid grounding in statistical techniques to further their careers. Jacco Thijssen is professor of finance at the University of York, UK. He holds a PhD in mathematical economics from Tilburg University, Netherlands. His main research interests are in applications of optimal stopping theory, stochastic calculus, and game theory to problems in economics and finance. Professor Thijssen has earned several awards for his statistics teaching.

New Developments in Statistical Modeling, Inference and Application

Автор: Jin
Название: New Developments in Statistical Modeling, Inference and Application
ISBN: 3319425706 ISBN-13(EAN): 9783319425702
Издательство: Springer
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Цена: 17819 р.
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Описание: The papers in this volume represent the most timely and advanced contributions to the 2014 Joint Applied Statistics Symposium of the International Chinese Statistical Association (ICSA) and the Korean International Statistical Society (KISS), held in Portland, Oregon. The contributions cover new developments in statistical modeling and clinical research: including model development, model checking, and innovative clinical trial design and analysis. Each paper was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe. It offered 3 keynote speeches, 7 short courses, 76 parallel scientific sessions, student paper sessions, and social events.

Essential Statistical Inference

Автор: Boos
Название: Essential Statistical Inference
ISBN: 1461448174 ISBN-13(EAN): 9781461448174
Издательство: Springer
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Цена: 16334 р.
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Описание: A superb resource on statistical inference for researchers or students, this book has R code throughout, including in sample problems, and an appendix of derived notation and formulae. It covers core topics as well as modern aspects such as M-estimation.

Methods for estimation and inference in modern econometrics

Автор: Anatolyev, Stanislav Gospodinov, Nikolay
Название: Methods for estimation and inference in modern econometrics
ISBN: 1439838240 ISBN-13(EAN): 9781439838242
Издательство: Taylor&Francis
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Цена: 15426 р.
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Описание:

Methods for Estimation and Inference in Modern Econometrics provides a comprehensive introduction to a wide range of emerging topics, such as generalized empirical likelihood estimation and alternative asymptotics under drifting parameterizations, which have not been discussed in detail outside of highly technical research papers. The book also addresses several problems often arising in the analysis of economic data, including weak identification, model misspecification, and possible nonstationarity. The book's appendix provides a review of some basic concepts and results from linear algebra, probability theory, and statistics that are used throughout the book.





Topics covered include:







  • Well-established nonparametric and parametric approaches to estimation and conventional (asymptotic and bootstrap) frameworks for statistical inference


  • Estimation of models based on moment restrictions implied by economic theory, including various method-of-moments estimators for unconditional and conditional moment restriction models, and asymptotic theory for correctly specified and misspecified models


  • Non-conventional asymptotic tools that lead to improved finite sample inference, such as higher-order asymptotic analysis that allows for more accurate approximations via various asymptotic expansions, and asymptotic approximations based on drifting parameter sequences






Offering a unified approach to studying econometric problems, Methods for Estimation and Inference in Modern Econometrics links most of the existing estimation and inference methods in a general framework to help readers synthesize all aspects of modern econometric theory. Various theoretical exercises and suggested solutions are included to facilitate understanding.

Statistical and Inductive Inference by Minimum Message Length

Автор: C.S. Wallace
Название: Statistical and Inductive Inference by Minimum Message Length
ISBN: 1441920153 ISBN-13(EAN): 9781441920157
Издательство: Springer
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Цена: 23016 р.
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Описание: Mythanksareduetothemanypeoplewhohaveassistedintheworkreported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi?cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di?erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re?ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi?cation of the sample was a way of brie?y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks arguing our cases, we decided on the maths for each approach, and soon discovered they gave essentially the same results. Without Boulton s insight, we may never have made the connection between inference and brief encoding, which is the heart of this work."

Автор: Sunil K. Mathur
Название: Probability and Statistical Inference Using R,
ISBN: 012386982X ISBN-13(EAN): 9780123869821
Издательство: Elsevier Science
Цена: 10536 р.
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Statistical Inference

Автор: Basu
Название: Statistical Inference
ISBN: 1420099655 ISBN-13(EAN): 9781420099652
Издательство: Taylor&Francis
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Цена: 25410 р.
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Описание: This book gives a comprehensive account of density-based minimum distance methods and their use in statistical inference. It covers statistical distances, density-based minimum distance methods, discrete and continuous models, asymptotic distributions, robustness, computational issues, residual adjustment functions, graphical descriptions of robustness, penalized and combined distances, multisample methods, weighted likelihood, and multinomial goodness-of-fit tests. The book also introduces the minimum distance methodology in interdisciplinary areas, such as neural networks and image processing, as well as specialized models and problems, including regression, mixture models, survival and Bayesian analysis, and more.

Introduction to Probability and Statistical Inference

Автор: Roussas George G
Название: Introduction to Probability and Statistical Inference
ISBN: 0128001143 ISBN-13(EAN): 9780128001141
Издательство: Elsevier Science
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Цена: 13358 р.
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Описание: An Introduction to Probability and Statistical Inference, Second Edition, guides you through probability models and statistical methods and helps you to think critically about various concepts. Written by award-winning author George Roussas, this book introduces readers with no prior knowledge in probability or statistics to a thinking process to help them obtain the best solution to a posed question or situation. It provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. This text contains an enhanced number of exercises and graphical illustrations where appropriate to motivate the reader and demonstrate the applicability of probability and statistical inference in a great variety of human activities. Reorganized material is included in the statistical portion of the book to ensure continuity and enhance understanding. Each section includes relevant proofs where appropriate, followed by exercises with useful clues to their solutions. Furthermore, there are brief answers to even-numbered exercises at the back of the book and detailed solutions to all exercises are available to instructors in an Answers Manual. This text will appeal to advanced undergraduate and graduate students, as well as researchers and practitioners in engineering, business, social sciences or agriculture.

Statistical Inference for Models with Multivariate T-distrib

Автор: Saleh A K Md Ehsanes
Название: Statistical Inference for Models with Multivariate T-distrib
ISBN: 1118854055 ISBN-13(EAN): 9781118854051
Издательство: Wiley
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Цена: 16326 р.
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Описание: Uniquely presents systematic analytical results using Student’s t –distributed errors in linear models Statistical Inference for Models with Multivariate t–Distributed Errors presents a wide array of applications for the analysis of multivariate observations and emphasizes the Student’s t –distribution method. The book illustrates the development of linear statistical models with applications to a variety of fields including mathematics, statistics, biostatistics, engineering, and the physical sciences. The book begins with a summary of the results under normal theory and proceeds to the statistical analysis of location models, simple regression, analysis of variance (ANOVA), parallelism, multiple regression, ridge regression, multivariate and simple multivariate linear models, and linear prediction. Providing a clear and balanced introduction to statistical inference, the bookalso features: A unique connection to normal distribution, Bayesian analysis, prediction problems, and Stein shrinkage estimation Practical real–world examples that address linear regression models with non–normal errors with practical real–world examples Plentiful applications and end–of–chapter problems that enhance the applications for the analysis of multivariate observations An up–to–date bibliography featuring the latest trends and advances to provide a collective resource for research Statistical Inference for Models with Multivariate t–Distributed Errors is an excellent upper–undergraduate and graduate–level textbook for courses in multivariate analysis, regression, linear models, and Bayesian analysis. The book is also a useful resource for statistical practitioners who need solid methodology within mathematical and quantitative statistics.


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