Автор: Wilcox, Rand R. (university Of Southern California, Usa) Название: Introduction to robust estimation and hypothesis testing ISBN: 0128200987 ISBN-13(EAN): 9780128200988 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Follow one girl as she builds a rocket and plans to take her friends on an amazing trip to the Sun and Moon. But will the task prove more difficult than she first thought? Imaginatively illustrated by T.S Spookytooth, this clever and inventive poem was written by eleven-year-old Collins Big Cat 2011 Writing Competition winner Nicole Sharrocks.
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.
Автор: Srivastava, Virendera K. , Giles, David E.A. Название: Seemingly Unrelated Regression Equations Models ISBN: 0367451484 ISBN-13(EAN): 9780367451486 Издательство: Taylor&Francis Рейтинг: Цена: 6736.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book brings together the scattered literature associated with the seemingly unrelated regression equations (SURE) model used by econometricians and others. It focuses on the theoretical statistical results associated with the SURE model.
Автор: Gonin, Rene Название: Nonlinear Lp-Norm Estimation ISBN: 0367451166 ISBN-13(EAN): 9780367451165 Издательство: Taylor&Francis Рейтинг: Цена: 9492.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book delineates the history of Lp-norm estimation and examines the nonlinear Lp-norm estimation problem that is a viable alternative to least squares estimation problems. It is intended for both statisticians and applied mathematicians.
Описание: Nonresponse and other sources of bias are endemic features of public opinion surveys. We elaborate a general workflow of weighting-based survey inference, and describe in detail how this can be applied to the analysis of historical and contemporary opinion polls.
Автор: Efromovich, Sam (ut Dallas, Richardson, Tx) Название: Missing and modified data in nonparametric estimation ISBN: 1138054887 ISBN-13(EAN): 9781138054882 Издательство: Taylor&Francis Рейтинг: Цена: 15004.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at random, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications. The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study. The book is primarily about practically important small samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent estimation impossible, then the author explains what type of action is needed to restore the lost information. The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively. Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of nonparametric curve estimation and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association.
Автор: Luca Oneto Название: Model Selection and Error Estimation in a Nutshell ISBN: 3030243583 ISBN-13(EAN): 9783030243586 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Поставка под заказ.
Описание: How can we select the best performing data-driven model? How can we rigorously estimate its generalization error? Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80’s and includes the most recent results. It discusses open problems and outlines future directions for research.
Автор: Azizur Rahman, Ann Harding Название: Small Area Estimation and Microsimulation Modeling ISBN: 036726126X ISBN-13(EAN): 9780367261269 Издательство: Taylor&Francis Рейтинг: Цена: 7501.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book gathers information on the theories, applications, advantages, and limitations of all the small area estimation methodologies. It covers direct small area estimation methods, indirect statistical approaches, including empirical best linear unbiased prediction, empirical Bayes and hierarchical Bayes estimation methods.
Автор: Eric Gautier and Pierre Alquier Название: Inverse problems and high-dimensional estimation ISBN: 3642199887 ISBN-13(EAN): 9783642199882 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The product of a high-flying summer school in Paris in 2009, this volume synthesises the state of the art on ill-posed statistical inverse problems and high-dimensional estimation and explores the ways these techniques can be applied to economics.
Автор: Gershon Eli Название: Advanced Topics in Control and Estimation of State-multiplic ISBN: 1447150694 ISBN-13(EAN): 9781447150695 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides readers with a coherent, structured account of state-multiplicative noisy systems. It demonstrates practical control engineering examples from various areas of the discipline to illustrate the relevance of the theoretical development.
Автор: Efron Название: Large-Scale Inference ISBN: 110761967X ISBN-13(EAN): 9781107619678 Издательство: Cambridge Academ Рейтинг: Цена: 6811.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Modern scientific technology (such as microarrays and fMRI machines) produces data in vast quantities. Bradley Efron explains the empirical Bayes methods that help make sense of a new statistical world. This is essential reading for professional statisticians and graduate students wishing to use and understand important new techniques like false discovery rates.
Автор: Alexandre B. Tsybakov Название: Introduction to Nonparametric Estimation ISBN: 0387790519 ISBN-13(EAN): 9780387790510 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Presents basic nonparametric regression and density estimators and analyzes their properties. This book covers minimax lower bounds, and develops advanced topics such as: Pinsker`s theorem, oracle inequalities, Stein shrinkage, and sharp minimax adaptivity.
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