Author: N. Balakrishna
Publisher: Springer Nature
ISBN: 9811681627
Category : Mathematics
Languages : en
Pages : 238
Book Description
This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minification models. Even though several non-Gaussian time-series models are available in the literature, most of them are focusing on the model structure and the probabilistic properties.
Non-Gaussian Autoregressive-Type Time Series
Author: N. Balakrishna
Publisher: Springer Nature
ISBN: 9811681627
Category : Mathematics
Languages : en
Pages : 238
Book Description
This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minification models. Even though several non-Gaussian time-series models are available in the literature, most of them are focusing on the model structure and the probabilistic properties.
Publisher: Springer Nature
ISBN: 9811681627
Category : Mathematics
Languages : en
Pages : 238
Book Description
This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minification models. Even though several non-Gaussian time-series models are available in the literature, most of them are focusing on the model structure and the probabilistic properties.
Non-Gaussian First-order Autoregressive Time Series Models
Author: Leanna Marisa Tedesco
Publisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages : 274
Book Description
Publisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages : 274
Book Description
Non-Linear Time Series
Author: Kamil Feridun Turkman
Publisher: Springer
ISBN: 3319070282
Category : Mathematics
Languages : en
Pages : 255
Book Description
This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included. Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time series.
Publisher: Springer
ISBN: 3319070282
Category : Mathematics
Languages : en
Pages : 255
Book Description
This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included. Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time series.
Smoothing Non-Gaussian Time Series with Autoregressive Structure
Author: G. K. Grunwald
Publisher:
ISBN:
Category : Nonparametric statistics
Languages : en
Pages : 23
Book Description
Publisher:
ISBN:
Category : Nonparametric statistics
Languages : en
Pages : 23
Book Description
Topics in Statistical Dependence
Author: Henry W. Block
Publisher: IMS
ISBN: 9780940600232
Category : Mathematical statistics
Languages : en
Pages : 558
Book Description
Publisher: IMS
ISBN: 9780940600232
Category : Mathematical statistics
Languages : en
Pages : 558
Book Description
Gaussian and Non-Gaussian Linear Time Series and Random Fields
Author: Murray Rosenblatt
Publisher: Springer
ISBN: 9781461270676
Category : Mathematics
Languages : en
Pages : 0
Book Description
The principal focus here is on autoregressive moving average models and analogous random fields, with probabilistic and statistical questions also being discussed. The book contrasts Gaussian models with noncausal or noninvertible (nonminimum phase) non-Gaussian models and deals with problems of prediction and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted. Intended as a text for gradutes in statistics, mathematics, engineering, the natural sciences and economics, the only recommendation is an initial background in probability theory and statistics. Notes on background, history and open problems are given at the end of the book.
Publisher: Springer
ISBN: 9781461270676
Category : Mathematics
Languages : en
Pages : 0
Book Description
The principal focus here is on autoregressive moving average models and analogous random fields, with probabilistic and statistical questions also being discussed. The book contrasts Gaussian models with noncausal or noninvertible (nonminimum phase) non-Gaussian models and deals with problems of prediction and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted. Intended as a text for gradutes in statistics, mathematics, engineering, the natural sciences and economics, the only recommendation is an initial background in probability theory and statistics. Notes on background, history and open problems are given at the end of the book.
Non-Gaussian structural time series models
Author: Cristiano Augusto Coelho Fernandes
Publisher:
ISBN:
Category :
Languages : en
Pages : 492
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 492
Book Description
Non-linear and Non-stationary Time Series Analysis
Author: Maurice Bertram Priestley
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 258
Book Description
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 258
Book Description
Scientific and Technical Aerospace Reports
Diagnostic Checks in Time Series
Author: Wai Keung Li
Publisher: CRC Press
ISBN: 1135441154
Category : Mathematics
Languages : en
Pages : 276
Book Description
Diagnostic checking is an important step in the modeling process. But while the literature on diagnostic checks is quite extensive and many texts on time series modeling are available, it still remains difficult to find a book that adequately covers methods for performing diagnostic checks. Diagnostic Checks in Time Series helps to fill that
Publisher: CRC Press
ISBN: 1135441154
Category : Mathematics
Languages : en
Pages : 276
Book Description
Diagnostic checking is an important step in the modeling process. But while the literature on diagnostic checks is quite extensive and many texts on time series modeling are available, it still remains difficult to find a book that adequately covers methods for performing diagnostic checks. Diagnostic Checks in Time Series helps to fill that