Nonlinear time series: theory, methods and applications with R examples

FOUNDATIONSLinear ModelsStochastic Processes The Covariance World Linear Processes The Multivariate Cases Numerical Examples ExercisesLinear Gaussian State Space Models Model Basics Filtering, Smoothing, and Forecasting Maximum Likelihood Estimation Smoothing Splines and the Kalman Smoother Asymptot...

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Detalles Bibliográficos
Autores principales: Douc, Randal, Moulines, Eric, Stoffer, David
Lenguaje:eng
Publicado: CRC Press 2014
Materias:
Mathematical Physics and Mathematics
Acceso en línea:http://cds.cern.ch/record/2018937
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author Douc, Randal
Moulines, Eric
Stoffer, David
author_facet Douc, Randal
Moulines, Eric
Stoffer, David
author_sort Douc, Randal
collection CERN
description FOUNDATIONSLinear ModelsStochastic Processes The Covariance World Linear Processes The Multivariate Cases Numerical Examples ExercisesLinear Gaussian State Space Models Model Basics Filtering, Smoothing, and Forecasting Maximum Likelihood Estimation Smoothing Splines and the Kalman Smoother Asymptotic Distribution of the MLE Missing Data Modifications Structural Component Models State-Space Models with Correlated Errors Exercises Beyond Linear ModelsNonlinear Non-Gaussian Data Volterra Series Expansion Cumulants and Higher-Order Spectra Bilinear Models Conditionally Heteroscedastic Models Thre
id cern-2018937
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2014
publisher CRC Press
record_format invenio
spelling cern-20189372021-04-21T20:18:33Zhttp://cds.cern.ch/record/2018937engDouc, RandalMoulines, EricStoffer, DavidNonlinear time series: theory, methods and applications with R examplesMathematical Physics and MathematicsFOUNDATIONSLinear ModelsStochastic Processes The Covariance World Linear Processes The Multivariate Cases Numerical Examples ExercisesLinear Gaussian State Space Models Model Basics Filtering, Smoothing, and Forecasting Maximum Likelihood Estimation Smoothing Splines and the Kalman Smoother Asymptotic Distribution of the MLE Missing Data Modifications Structural Component Models State-Space Models with Correlated Errors Exercises Beyond Linear ModelsNonlinear Non-Gaussian Data Volterra Series Expansion Cumulants and Higher-Order Spectra Bilinear Models Conditionally Heteroscedastic Models ThreCRC Pressoai:cds.cern.ch:20189372014
spellingShingle Mathematical Physics and Mathematics
Douc, Randal
Moulines, Eric
Stoffer, David
Nonlinear time series: theory, methods and applications with R examples
title Nonlinear time series: theory, methods and applications with R examples
title_full Nonlinear time series: theory, methods and applications with R examples
title_fullStr Nonlinear time series: theory, methods and applications with R examples
title_full_unstemmed Nonlinear time series: theory, methods and applications with R examples
title_short Nonlinear time series: theory, methods and applications with R examples
title_sort nonlinear time series: theory, methods and applications with r examples
topic Mathematical Physics and Mathematics
url http://cds.cern.ch/record/2018937
work_keys_str_mv AT doucrandal nonlineartimeseriestheorymethodsandapplicationswithrexamples
AT moulineseric nonlineartimeseriestheorymethodsandapplicationswithrexamples
AT stofferdavid nonlineartimeseriestheorymethodsandapplicationswithrexamples

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