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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
Классификация:
Эконометрика
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: 0.466 кг.
Дата издания: 06.06.2011
Иллюстрации: 50 black & white illustrations
Размер: 243 x 152 x 18
Читательская аудитория: Postgraduate, research & scholarly
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Поставляется из: Англии
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Склад Англия: 28шт.  Склад Америка: 75шт.  
При оформлении заказа до: 6 янв 2018
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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: 3593 р.
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Описание: In this groundbreaking text, two world-renowned experts present statistical methods for studying causal effects: how can we learn about the expected effect of an intervention or a change in environment? The authors discuss how we can assess such effects in simple randomized experiments, where the researcher controls the treatments, and in observational studies, where the subjects may themselves affect which treatment they receive.

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Simulation-based Inference in Econometrics

Автор: Mariano, Roberto S.
Название: Simulation-based Inference in Econometrics
ISBN: 052108802X ISBN-13(EAN): 9780521088022
Издательство: Cambridge Academ
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Цена: 2804 р.
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Описание: An overview of the techniques and practices involved in simulation-based inference.

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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
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Цена: 6772 р.
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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
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Цена: 6541 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. 

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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
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Цена: 14244 р.
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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.

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Simultaneous Inference in Regression

Автор: Liu
Название: Simultaneous Inference in Regression
ISBN: 1439828091 ISBN-13(EAN): 9781439828090
Издательство: Taylor&Francis
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Цена: 6863 р.
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Описание: With examples and MATLAB® programs that make it easy to apply the methods to your own data analysis, this book provides a thorough overview of the construction methods and applications of simultaneous confidence bands for various inferential purposes. Most of the text covers normal-error linear regression models, although the author also describes the logistic regression model to show how simultaneous confidence bands can be constructed and used for generalized linear regression models. The MATLAB programs, along with color figures, are available for download on the author’s website.

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An Introduction to Statistical Inference and Its Applications with R

Автор: Trosset
Название: An Introduction to Statistical Inference and Its Applications with R
ISBN: 1584889470 ISBN-13(EAN): 9781584889472
Издательство: Taylor&Francis
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Цена: 5279 р.
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Описание: With amusing anecdotes and trivia, this text explains how statistical methods are used for data analysis and uses the elementary functions of R to perform the individual steps of statistical procedures. It introduces basic concepts of inference through a careful study of several important procedures, including parametric and nonparametric methods, analysis of variance, and regression. The text also presents many applications, supporting data sets, and end-of-chapter exercises. The R code and data sets are available for download online and a solutions manual is available for qualifying instructors.

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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: 6511 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.

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Asymptotic theory of quantum statistical inference: selected papers

Название: Asymptotic theory of quantum statistical inference: selected papers
ISBN: 9812560157 ISBN-13(EAN): 9789812560155
Издательство: World Scientific Publishing
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Цена: 16565 р.
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Описание: Quantum statistical inference, a research field with deep roots in the foundations of both quantum physics and mathematical statistics, has made remarkable progress since 1990. In particular, its asymptotic theory has been developed during this period. However, there has hitherto been no book covering this remarkable progress after 1990; the famous textbooks by Holevo and Helstrom deal only with research results in the earlier stage (1960s-1970s).

This book presents the important and recent results of quantum statistical inference. It focuses on the asymptotic theory, which is one of the central issues of mathematical statistics and had not been investigated in quantum statistical inference until the early 1980s. It contains outstanding papers after Holevo's textbook, some of which are of great importance but are not available now.

The reader is expected to have only elementary mathematical knowledge, and therefore much of the content will be accessible to graduate students as well as research workers in related fields. Introductions to quantum statistical inference have been specially written for the book. Asymptotic Theory of Quantum Statistical Inference: Selected Papers will give the reader a new insight into physics and statistical inference.

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Nonparametric inference

Название: Nonparametric inference
ISBN: 981270034X ISBN-13(EAN): 9789812700346
Издательство: World Scientific Publishing
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Цена: 15338 р.
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Описание: This book provides a solid foundation on nonparametric inference for students taking a graduate course in nonparametric statistics and serves as an easily accessible source for researchers in the area. With the exception of some sections requiring familiarity with measure theory, readers with an advanced calculus background will be comfortable with the material.

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Advances in statistical modeling and inference: essays in honor of kjell a doksum

Название: Advances in statistical modeling and inference: essays in honor of kjell a doksum
ISBN: 9812703691 ISBN-13(EAN): 9789812703699
Издательство: World Scientific Publishing
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Цена: 25564 р.
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Описание: There have been major developments in the field of statistics over the last quarter century, spurred by the rapid advances in computing and data-measurement technologies. These developments have revolutionized the field and have greatly influenced research directions in theory and methodology. Increased computing power has spawned entirely new areas of research in computationally-intensive methods, allowing us to move away from narrowly applicable parametric techniques based on restrictive assumptions to much more flexible and realistic models and methods.

These computational advances have also led to the extensive use of simulation and Monte Carlo techniques in statistical inference. All of these developments have, in turn, stimulated new research in theoretical statistics. This volume provides an up-to-date overview of recent advances in statistical modeling and inference.

Written by renowned researchers from across the world, it discusses flexible models, semi-parametric methods and transformation models, nonparametric regression and mixture models, survival and reliability analysis, and re-sampling techniques. With its coverage of methodology and theory as well as applications, the book is an essential reference for researchers, graduate students, and practitioners.

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Modelling Operational Risk Using Bayesian Inference

Автор: Shevchenko
Название: Modelling Operational Risk Using Bayesian Inference
ISBN: 3642159222 ISBN-13(EAN): 9783642159220
Издательство: Springer
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Цена: 9239 р.
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Описание: The management of operational risk in the banking industry has undergone explosive changes over the last decade due to substantial changes in the operational environment. Globalization, deregulation, the use of complex financial products, and changes in information technology have resulted in exposure to new risks which are very different from market and credit risks. In response, the Basel Committee on Banking Supervision has developed a new regulatory framework for capital measurement and standards for the banking sector. This has formally defined operational risk and introduced corresponding capital requirements.Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses. There are a number of unresolved methodological challenges in the LDA implementation. Overall, the area of quantitative operational risk is very new and different methods are under hot debate.This book is devoted to quantitative issues in LDA. In particular, the use of Bayesian inference is the main focus. Though it is very new in this area, the Bayesian approach is well suited for modelling operational risk, as it allows for a consistent and convenient statistical framework for quantifying the uncertainties involved. It also allows for the combination of expert opinion with historical internal and external data in estimation procedures. These are critical, especially for low-frequency/high-impact operational risks.This book is aimed at practitioners in risk management, academic researchers in financial mathematics, banking industry regulators and advanced graduate students in the area. It is a must-read for anyone who works, teaches or does research in the area of financial risk.

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