Handbook for Applied Modeling: Non-Gaussian and Correlated Data, Jamie D. Riggs, Trent L. Lalonde
Автор: Jeffrey R. Wilson; Kent A. Lorenz Название: Modeling Binary Correlated Responses using SAS, SPSS and R ISBN: 3319373617 ISBN-13(EAN): 9783319373614 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Statistical tools to analyze correlated binary data are spread out in the existing literature. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book.
Автор: Simon Marvin K., Riedel Eibe Название: Probability Distributions Involving Gaussian Random Variables / A Handbook for Engineers and Scientists ISBN: 0387346570 ISBN-13(EAN): 9780387346571 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This handbook brings together a comprehensive collection of mathematical material in one location. It also offers a variety of new results interpreted in a form that is particularly useful to engineers, scientists, and applied mathematicians.
Автор: V?ronique Gayrard; Nicola Kistler Название: Correlated Random Systems: Five Different Methods ISBN: 3319176730 ISBN-13(EAN): 9783319176734 Издательство: Springer Рейтинг: Цена: 41920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume presents five different methods recently developed to tackle the large scale behavior of highly correlated random systems, such as spin glasses, random polymers, local times and loop soups and random matrices.
Автор: Jamie D. Riggs Название: Handbook for Applied Modeling: Non-Gaussian and Correlated Data ISBN: 1316601056 ISBN-13(EAN): 9781316601051 Издательство: Cambridge Academ Рейтинг: Цена: 40130.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing data that fail idealized assumptions. It explains and demonstrates core techniques, common pitfalls and data issues, and interpretation of model results, all with a focus on application, utility, and real-life data.
Автор: Timothy G. Gregoire; David R. Brillinger; Peter Di Название: Modelling Longitudinal and Spatially Correlated Data ISBN: 0387982167 ISBN-13(EAN): 9780387982168 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This refereed volume includes papers presented at a conference on modelling longitudinal and spatially correlated data. Many of the best researchers in the world have presented papers in an area with important applications to biostatistics and the environmental sciences.
Автор: Sutradhar Название: Advances and Challenges in Parametric and Semi-parametric Analysis for Correlated Data ISBN: 3319312588 ISBN-13(EAN): 9783319312583 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This proceedings volume contains eight selected papers thatwere presented in the International Symposium in Statistics (ISS) 2015 OnAdvances in Parametric and Semi-parametric Analysis of Multivariate, TimeSeries, Spatial-temporal, and Familial-longitudinal Data, held in St. John`s,Canada from July 6 to 8, 2015.
Название: Generalized Linear and Nonlinear Models for Correlated Data ISBN: 1599946475 ISBN-13(EAN): 9781599946474 Издательство: Неизвестно Рейтинг: Цена: 143390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Edward F. Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.
Автор: Paul Knottnerus Название: Linear Models with Correlated Disturbances ISBN: 3540539018 ISBN-13(EAN): 9783540539018 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Looks at the estimation of regression models with correlated disturbances. Topics discussed include maximum likelihood, test strategies, Kalman filtering, conditional normal distributions, the Cramer-Rao inequality, and Cholesky decomposition. A simple geometrical approach is used.
Автор: Jondeau Eric Название: Financial Modeling Under Non-Gaussian Distributions ISBN: 1849965994 ISBN-13(EAN): 9781849965996 Издательство: Springer Цена: 83850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Practitioners and researchers who have handled financial market data know that asset returns do not behave according to the bell-shaped curve, associated with the Gaussian or normal distribution. Indeed, the use of Gaussian models when the asset return distributions are not normal could lead to a wrong choice of portfolio, the underestimation of extreme losses or mispriced derivative products. Consequently, non-Gaussian models and models based on processes with jumps, are gaining popularity among financial market practitioners.
Non-Gaussian distributions are the key theme of this book which addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. One of the main aims is to bridge the gap between the theoretical developments and the practical implementations of what many users and researchers perceive as "sophisticated" models or black boxes. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series, such as exchange and interest rates.
The authors have taken care to make the material accessible to anyone with a basic knowledge of statistics, calculus and probability, while at the same time preserving the mathematical rigor and complexity of the original models.
This book will be an essential reference for practitioners in the finance industry, especially those responsible for managing portfolios and monitoring financial risk, but it will also be useful for mathematicians who want to know more about how their mathematical tools are applied in finance, and as a text for advanced courses in empirical finance; financial econometrics and financial derivatives.
Автор: Rue Название: Gaussian Markov Random Fields ISBN: 1584884320 ISBN-13(EAN): 9781584884323 Издательство: Taylor&Francis Рейтинг: Цена: 163330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Gaussian Markov Random Field (GMRF) models, most widely used in spatial statistics are presented in this, the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects.
Автор: Murray Rosenblatt Название: Gaussian and Non-Gaussian Linear Time Series and Random Fields ISBN: 1461270677 ISBN-13(EAN): 9781461270676 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The principal focus here is on autoregressive moving average models and analogous random fields, with probabilistic and statistical questions also being discussed.
Автор: Jondeau Название: Financial Modeling Under Non-Gaussian Distributions ISBN: 1846284198 ISBN-13(EAN): 9781846284199 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The use of Gaussian models when the asset return distributions are not normal could lead to a wrong choice of portfolio, the underestimation of extreme losses or mispriced derivative products. This book deals with the non-Gaussian distributions and addresses the consequences of non-normality and time dependency in asset returns and option prices.
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