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Nonlinear Estimation, Gavin J.S. Ross


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Автор: Gavin J.S. Ross
Название:  Nonlinear Estimation
ISBN: 9781461280019
Издательство: Springer
Классификация:

ISBN-10: 146128001X
Обложка/Формат: Paperback
Страницы: 189
Вес: 0.29 кг.
Дата издания: 08.10.2011
Серия: Springer Series in Statistics
Язык: English
Размер: 234 x 156 x 11
Основная тема: Mathematics
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: The book provides insights into why some models are difficult to fit, how to combine fits over different data sets, how to improve data collection to reduce prediction variance, and how to program particular models to handle a full range of data sets.

Recursive Nonlinear Estimation

Автор: Rudolph Kulhavy
Название: Recursive Nonlinear Estimation
ISBN: 3540760636 ISBN-13(EAN): 9783540760634
Издательство: Springer
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Цена: 95770.00 T
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Описание: In a close analogy to matching data in Euclidean space, this monograph views parameter estimation as matching of the empirical distribution of data with a model-based distribution. The book suggests a solution to the problem of recursive estimation of non-Gaussian and nonlinear models.

Nonlinear Estimation and Classification

Автор: David D. Denison; Mark H. Hansen; Christopher C. H
Название: Nonlinear Estimation and Classification
ISBN: 0387954716 ISBN-13(EAN): 9780387954714
Издательство: Springer
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Цена: 81050.00 T
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Описание: Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing.

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: 102080.00 T
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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.


Introduction to Nonparametric Estimation

Автор: Alexandre B. Tsybakov
Название: Introduction to Nonparametric Estimation
ISBN: 0387790519 ISBN-13(EAN): 9780387790510
Издательство: Springer
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Цена: 102480.00 T
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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.

Inverse problems and high-dimensional estimation

Автор: Eric Gautier and Pierre Alquier
Название: Inverse problems and high-dimensional estimation
ISBN: 3642199887 ISBN-13(EAN): 9783642199882
Издательство: Springer
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Цена: 102480.00 T
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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.

Nonlinear Pricing Methods in Quantitative Finance

Автор: Guyon
Название: Nonlinear Pricing Methods in Quantitative Finance
ISBN: 1466570334 ISBN-13(EAN): 9781466570337
Издательство: Taylor&Francis
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Цена: 183750.00 T
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Описание:

New Tools to Solve Your Option Pricing Problems

For nonlinear PDEs encountered in quantitative finance, advanced probabilistic methods are needed to address dimensionality issues. Written by two leaders in quantitative research--including Risk magazine's 2013 Quant of the Year--Nonlinear Option Pricing compares various numerical methods for solving high-dimensional nonlinear problems arising in option pricing. Designed for practitioners, it is the first authored book to discuss nonlinear Black-Scholes PDEs and compare the efficiency of many different methods.

Real-World Solutions for Quantitative Analysts

The book helps quants develop both their analytical and numerical expertise. It focuses on general mathematical tools rather than specific financial questions so that readers can easily use the tools to solve their own nonlinear problems. The authors build intuition through numerous real-world examples of numerical implementation. Although the focus is on ideas and numerical examples, the authors introduce relevant mathematical notions and important results and proofs. The book also covers several original approaches, including regression methods and dual methods for pricing chooser options, Monte Carlo approaches for pricing in the uncertain volatility model and the uncertain lapse and mortality model, the Markovian projection method and the particle method for calibrating local stochastic volatility models to market prices of vanilla options with/without stochastic interest rates, the a + bλ technique for building local correlation models that calibrate to market prices of vanilla options on a basket, and a new stochastic representation of nonlinear PDE solutions based on marked branching diffusions.


Theory and Estimation of Macroeconomic Rationing Models

Автор: H.R. Sneessens
Название: Theory and Estimation of Macroeconomic Rationing Models
ISBN: 3540108378 ISBN-13(EAN): 9783540108375
Издательство: Springer
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Цена: 102480.00 T
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Описание: Introducing his book "The Theory of UnemPloyment Reconsidered", Professor Malinvaud expressed several years ago his hope "to convey [his] strong belief that the reconsideration is a major step in the development of our science".

Analysis and Estimation of Stochastic Mechanical Systems

Автор: Werner Schiehlen; Walter Wedig
Название: Analysis and Estimation of Stochastic Mechanical Systems
ISBN: 3211820582 ISBN-13(EAN): 9783211820582
Издательство: Springer
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Цена: 81050.00 T
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Описание: The main aspects of the course are random vibrations of discrete and continuous systems, analysis of nonlinear and parametric systems, stochastic modelling of fatigue damage, parameter estimation and identification with applications to vehicle road systems and process simulations by means of autoregressive models.

Nonparametric Estimation of Probability Densities and Regression Curves

Автор: Nadaraya
Название: Nonparametric Estimation of Probability Densities and Regression Curves
ISBN: 9027727570 ISBN-13(EAN): 9789027727572
Издательство: Springer
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Цена: 88500.00 T
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Описание: 'Et moi, ..., si. j'avail su comment en revenir. One service mathematics has rendered be human race. It has put common sense back jc n'y scrais point a1U: where it belongs, on the topmost sbelf next Jules Verne to \be dusty canister labelled 'discarded non- TIle series is divergent; therefore we may be sense'. able to do something with it Eric T. Bell O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non- linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic bas rendered com- puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.

Estimation of Dependences Based on Empirical Data

Автор: V. Vapnik; S. Kotz
Название: Estimation of Dependences Based on Empirical Data
ISBN: 1441921583 ISBN-13(EAN): 9781441921581
Издательство: Springer
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Цена: 172350.00 T
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Описание: Afterword of 2006

Principles of Signal Detection and Parameter Estimation

Автор: Bernard C. Levy
Название: Principles of Signal Detection and Parameter Estimation
ISBN: 1441945652 ISBN-13(EAN): 9781441945655
Издательство: Springer
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Цена: 69650.00 T
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Описание: This comprehensive text addresses signal processing and communication applications with an emphasis on fundamental principles. It also looks at recent advances in the field such as sequential testing, Gaussian and Robust detection, and detection of Markov Chains.

Parameter Estimation and Hypothesis Testing in Linear Models

Автор: Karl-Rudolf Koch
Название: Parameter Estimation and Hypothesis Testing in Linear Models
ISBN: 3642084613 ISBN-13(EAN): 9783642084614
Издательство: Springer
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Цена: 97780.00 T
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Описание: Readers will find here presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model as well as the mixed model for estimating random parameters.


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