Robust And Nonlinear Time Series Analysis
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Robust And Nonlinear Time Series Analysis
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Author :
language : en
Publisher:
Release Date : 1984
Robust And Nonlinear Time Series Analysis written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with categories.
Robust And Nonlinear Time Series Analysis
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Author : J. Franke
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Robust And Nonlinear Time Series Analysis written by J. Franke and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to "second order" has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica tion. They have none-the-less stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum ed model.
Robust And Nonlinear Time Series Analysis
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Author : Jürgen Franke
language : en
Publisher:
Release Date : 1984
Robust And Nonlinear Time Series Analysis written by Jürgen Franke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with Estadística - Congresos categories.
Nonlinear Time Series
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Author : Jianqing Fan
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-11
Nonlinear Time Series written by Jianqing Fan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-11 with Mathematics categories.
This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.
Robust Multivariate And Nonlinear Time Series Models
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Author : Ravi Ramakrishnan
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2010
Robust Multivariate And Nonlinear Time Series Models written by Ravi Ramakrishnan and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of the S-estimator, a robust estimator, applied to some simple vector and nonlinear time series models. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator.
Non Linear And Non Stationary Time Series Analysis
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Author : Maurice Bertram Priestley
language : en
Publisher:
Release Date : 1988
Non Linear And Non Stationary Time Series Analysis written by Maurice Bertram Priestley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Mathematics categories.
Nonlinear Time Series Analysis With R
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Author : Ray C. Huffaker
language : en
Publisher:
Release Date : 2017
Nonlinear Time Series Analysis With R written by Ray C. Huffaker and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Nonlinear theories categories.
A practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces.
Nonlinear Time Series
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Author : Jiti Gao
language : en
Publisher: CRC Press
Release Date : 2007-03-22
Nonlinear Time Series written by Jiti Gao and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-22 with Mathematics categories.
Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully
Nonlinear Time Series Analysis With R
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Author : Ray Huffaker
language : en
Publisher: Oxford University Press
Release Date : 2017-10-20
Nonlinear Time Series Analysis With R written by Ray Huffaker and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-20 with Mathematics categories.
Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. It joins the chorus of voices recommending 'getting to know your data' as an essential preliminary evidentiary step in modelling. Time series are often highly fluctuating with a random appearance. Observed volatility is commonly attributed to exogenous random shocks to stable real-world systems. However, breakthroughs in nonlinear dynamics raise another possibility: highly complex dynamics can emerge endogenously from astoundingly parsimonious deterministic nonlinear models. Nonlinear Time Series Analysis (NLTS) is a collection of empirical tools designed to aid practitioners detect whether stochastic or deterministic dynamics most likely drive observed complexity. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their modelling approach. This book is targeted to professionals and graduate students in engineering and the biophysical and social sciences. Its major objectives are to help non-mathematicians — with limited knowledge of nonlinear dynamics — to become operational in NLTS; and in this way to pave the way for NLTS to be adopted in the conventional empirical toolbox and core coursework of the targeted disciplines. Consistent with modern trends in university instruction, the book makes readers active learners with hands-on computer experiments in R code directing them through NLTS methods and helping them understand the underlying logic (please see www.marco.bittelli.com). The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework — condensed from sound empirical practices recommended in the literature — that details a step-by-step procedure for applying NLTS in real-world data diagnostics.
Robust Nonlinear Data Smoothers
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Author : Paul F. Velleman
language : en
Publisher:
Release Date : 1977
Robust Nonlinear Data Smoothers written by Paul F. Velleman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1977 with categories.