Dynamic time series models using r-inla, Ravishanker, Nalini (university Of Connecticut, Storrs, Usa) Raman, Balaji (cogitaas Ava, Mumbai, India) Soyer, Refik
Автор: Marta Blangiardo,Michela Cameletti Название: Spatial and Spatio–temporal Bayesian Models with R – INLA ISBN: 1118326555 ISBN-13(EAN): 9781118326558 Издательство: Wiley Рейтинг: Цена: 9496.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics.
Описание: The Integrated Nested Laplace Approximation is a popular method for approximate Bayesian inference. INLA is an alternative to other methods for Bayesian inference, such as Markov Chain Monte Carlo, that are more computationally demanding. In addition, the R-INLA package for the R statistical software provides a way to fit such models in practice
Автор: Gomez-rubio, Virgilio Название: Bayesian inference with inla ISBN: 1032174536 ISBN-13(EAN): 9781032174532 Издательство: Taylor&Francis Рейтинг: Цена: 6889.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Integrated Nested Laplace Approximation (INLA) is a popular method for approximate Bayesian inference. This book provides an introduction to the underlying INLA methodology and practical guidance on how to fit different models with R-INLA and R. This covers a wide range of applications, such as multilevel models, spatial models and survival
Автор: Frantisek Stulajter Название: Predictions in Time Series Using Regression Models ISBN: 1441929657 ISBN-13(EAN): 9781441929655 Издательство: Springer Рейтинг: Цена: 13415.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will interest and assist people who are dealing with the problems of predictions of time series in higher education and research. It will greatly assist people who apply time series theory to practical problems in their work and also serve as a textbook for postgraduate students in statistics economics and related subjects.
Описание: This book takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results.
Автор: Zucchini Название: Hidden Markov Models for Time Series ISBN: 1482253836 ISBN-13(EAN): 9781482253832 Издательство: Taylor&Francis Рейтинг: Цена: 14851.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Hidden Markov Models (HMMs) remains a vibrant area of research in statistics, with many new applications appearing since publication of the first edition.
Описание: The theory of time series models has been well developed over the last thirt,y years. The most interesting feature of such a model is that its second order covariance analysis is ve~ similar to that for a linear model. This demonstrates the importance of higher order covariance analysis for nonlinear models.
Автор: H. Tong Название: Threshold Models in Non-linear Time Series Analysis ISBN: 0387909184 ISBN-13(EAN): 9780387909189 Издательство: Springer Рейтинг: Цена: 14635.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the last two years or so, I was most fortunate in being given opportunities of lecturing on a new methodology to a variety of audiences in Britain, China, Finland, France and Spain.
Автор: Zucchini, Walter (university Of Gottingen, Germany) Macdonald, Iain L. Langrock, Roland Название: Hidden markov models for time series ISBN: 103217949X ISBN-13(EAN): 9781032179490 Издательство: Taylor&Francis Рейтинг: Цена: 6889.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Hidden Markov Models (HMMs) remains a vibrant area of research in statistics, with many new applications appearing since publication of the first edition.
Описание: This book provides statistical methodologies for time series data, focusing on copula-based Markov chain models for serially correlated time series.
Описание: The first two chapters are devoted to the basic theory of nonlinear functions of stationary Gaussian processes, Hermite polynomials, cumulants and higher order spectra, multiple Wiener-Ito integrals and finally chaotic Wiener-Ito spectral representation of subordinated processes.
Автор: Tunnicliffe Wilson Granville, Reale Marco, Haywood John Название: Models for Dependent Time Series ISBN: 1584886501 ISBN-13(EAN): 9781584886501 Издательство: Taylor&Francis Рейтинг: Цена: 14545.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.
The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational material for the remaining chapters, which cover the construction of structural models and the extension of vector autoregressive modeling to high frequency, continuously recorded, and irregularly sampled series. The final chapter combines these approaches with spectral methods for identifying causal dependence between time series. Web Resource A supplementary website provides the data sets used in the examples as well as documented MATLAB(R) functions and other code for analyzing the examples and producing the illustrations. The site also offers technical details on the estimation theory and methods and the implementation of the models.
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