Автор: Anatolyev, Stanislav Gospodinov, Nikolay Название: Methods for estimation and inference in modern econometrics ISBN: 1439838240 ISBN-13(EAN): 9781439838242 Издательство: Taylor&Francis Рейтинг: Цена: 15312.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Описание: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
Автор: Boos Название: Essential Statistical Inference ISBN: 1461448174 ISBN-13(EAN): 9781461448174 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Поставка под заказ.
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.
Автор: Box, George E. P. Tiao, George C. Название: Bayesian inference in statistical analysis ISBN: 0471574287 ISBN-13(EAN): 9780471574286 Издательство: Wiley Рейтинг: Цена: 25494.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Edited by Kim-Anh Do Название: Bayesian Inference for Gene Expression and Proteomics ISBN: 052186092X ISBN-13(EAN): 9780521860925 Издательство: Cambridge Academ Рейтинг: Цена: 11405.00 р. Наличие на складе: Поставка под заказ.
Описание: The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.
Автор: Dana Kelly; Curtis Smith Название: Bayesian Inference for Probabilistic Risk Assessment ISBN: 1447127080 ISBN-13(EAN): 9781447127086 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book synthesizes significant recent advances in the use of risk analysis in many government agencies and private corporations, providing a Bayesian foundation for framing probabilistic problems and performing inference on these problems.
Описание: "This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation" Dr.
Автор: Ghosal, Subhashis. Название: Fundamentals of Nonparametric Bayesian Inference ISBN: 0521878268 ISBN-13(EAN): 9780521878265 Издательство: Cambridge Academ Рейтинг: Цена: 12989.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses.
Название: Probability Theory and Statistical Inference ISBN: 0521424089 ISBN-13(EAN): 9780521424080 Издательство: Cambridge Academ Рейтинг: Цена: 7285.00 р. Наличие на складе: Поставка под заказ.
Описание: This major new textbook from a distinguished econometrician is intended for students taking introductory courses in probability theory and statistical inference. No prior knowledge other than a basic familiarity with descriptive statistics is assumed. The primary objective of this book is to establish the framework for the empirical modelling of observational (non-experimental) data. This framework known as 'Probabilistic Reduction' is formulated with a view to accommodating the peculiarities of observational (as opposed to experimental) data in a unifying and logically coherent way. Probability Theory and Statistical Inference differs from traditional textbooks in so far as it emphasizes concepts, ideas, notions and procedures which are appropriate for modelling observational data. Aimed at students at second-year undergraduate level and above studying econometrics and economics, this textbook will also be useful for students in other disciplines which make extensive use of observational data, including finance, biology, sociology and psychology and climatology.
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