Download or read book Non Gaussian Autoregressive Type Time Series written by N. Balakrishna and published by Springer Nature. This book was released on 2022-01-27 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Non Gaussian First order Autoregressive Time Series Models written by Leanna Marisa Tedesco and published by . This book was released on 1995 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Smoothing Non Gaussian Time Series with Autoregressive Structure written by G. K. Grunwald and published by . This book was released on 1994 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Topics in Statistical Dependence written by Henry W. Block and published by IMS. This book was released on 1990 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Gaussian and Non Gaussian Linear Time Series and Random Fields written by Murray Rosenblatt and published by Springer. This book was released on 2012-09-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Non linear and Non stationary Time Series Analysis written by Maurice Bertram Priestley and published by . This book was released on 1988 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Non Gaussian structural time series models written by Cristiano Augusto Coelho Fernandes and published by . This book was released on 1992 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1983 with total page 1368 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Diagnostic Checks in Time Series written by Wai Keung Li and published by CRC Press. This book was released on 2003-12-29 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Autoregressive Spectral Estimation and Functional Inference written by Emanuel Parzen and published by . This book was released on 1982 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: Functions used to describe the probability distributions of time series (both Gaussian and non-Gaussian) are introduced. The concept of type of a time series is defined. Autoregressive spectral densities are defined. Order determining criteria are motivated. through the concept of model identification by estimating information. An approach to empirical spectral analysis is suggested. (Author).
Download or read book MIXTURE AUTOREGRESSION W HEAVY written by Po-Ling Kam and published by Open Dissertation Press. This book was released on 2017-01-27 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Mixture Autoregression With Heavy-tailed Conditional Distribution" by Po-ling, Kam, 甘寶玲, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled MIXTURE AUTOREGRESSION WITH HEAVY-TAILED CONDITIONAL DISTRIBUTION submitted by KAM, Po Ling for the degree of Master of Philosophy at the University of Hong Kong in August 2003 In this thesis, we consider two types of the mixture autoregressive model. The first one is Gaussian mixture autoregressive model (GMAR) which is introduced by Wong and Li (2000). The second one is Student t-type MAR (TMAR) model which is a new model proposed by us. For GMAR model, it consists of a mixture K Gaussian autoregressive compo- nents. There are several properties which make Gaussian MAR model potentially useful in non-linear time series modelling. Firstly, it can be shown that a mixture of a non-stationary AR component with a stationary AR component can result in an overall stationary process. Secondly, the Gaussian MAR model can capture the shape-changing feature in conditional distribution of the time series. Lastly, the Gaussian MAR model can also capture conditional heteroscedasticity (Engle, 1982) which is a common phenomenon in financial time series. Despite the advantages of Gaussian MAR models, there are some limitations iin some real life applications. If densities of some financial time series data are plotted, it can be noticed that the densities tend to be fatter tailed than the normal. Moreover, extreme data are observed more often than those implied by a Gaussian distribution. According to Peel and McLachlan (2000), heavy tails and outliers affect the estimation of means and variances in mixture type models. The applicability of the Gaussian MAR model to financial time series might be questionable. In order to illustrate the problem, we perform several simulation studies to study the estimation of Gaussian MAR model using data generated from heavy tailed distributions. On the other hand, we introduced a new model called Student t-type MAR model. We propose to replace the normal conditional distribution in each com- ponent of the Gaussian MAR model by the Student t distribution. There is a parameter called degree of freedom which can be used to adjust the degree of heavy-tailness of the conditional distributions according to our need. As the de- gree of freedom in a Student t distribution approaches infinity, the distribution approaches normal. Hence, the Gaussian MAR model is a limiting case of the proposed Student t-type MAR model. The parameter estimation of TMAR model can be carried out via the EM al- gorithm (Dempster et al., 1977). The standard errors of the parameter estimates can be computed by the Missing Information Principle (Louis, 1982). For model selection, corrected Bayesian information criteria (BIC ) is adopted. Several simulation studies are preformed to illustrate the importance of correct choice of model when the true data generating process is a Student t-type MAR model. iiWe compare the performance of Gaussian and Student t-type MAR model by some simulation studies and real life examples. Several financial time series are employed to illustrate the usefulness of the Student t-type MAR model. (460 words) iii DOI: 10.5353/th_b2961492 Subjects: Autoregression (Statistics) Distribution (Probability theory) Time-series analysis Gaussian processes Finance - Statistical methods
Download or read book Biometrika written by D. M. Titterington and published by . This book was released on 2001 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: The year 2001 marks the centenary of Biometrika, one of the world's leading academic journals in statistical theory and methodology. In celebration of this, the book brings together two sets of papers from the journal. The first comprises seven specially commissioned articles (authors: D.R. Cox, A.C. Davison, Anthony C. Atkinson and R.A. Bailey, David Oakes, Peter Hall, T.M.F. Smith, and Howell Tong). These articles review the history of the journal and the most important contributions made by appearing in the journal in a number of important areas of statitisical activity, including general theory and methodology, surveys and time sets. In the process the papers describe the general development of statistical science during the twentieth century. The second group of ten papers are a selection of particularly seminal articles form the journal's first hundred years. The book opens with an introduction by the editors Professor D.M. Titterington and Sir David Cox.
Download or read book A Generalized Family of Time Series Models for Non Gaussian Data written by Michael Benjamin and published by . This book was released on 1999 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonlinear Time Series Analysis written by Ruey S. Tsay and published by John Wiley & Sons. This book was released on 2018-09-14 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: • Offers research developed by leading scholars of time series analysis • Presents R commands making it possible to reproduce all the analyses included in the text • Contains real-world examples throughout the book • Recommends exercises to test understanding of material presented • Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.
Download or read book Non Linear Time Series written by Kamil Feridun Turkman and published by Springer. This book was released on 2014-09-29 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Structural Vector Autoregressive Analysis written by Lutz Kilian and published by Cambridge University Press. This book was released on 2017-11-23 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.
Download or read book Topics in Non Gaussian Signal Processing written by Edward J. Wegman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-Gaussian Signal Processing is a child of a technological push. It is evident that we are moving from an era of simple signal processing with relatively primitive electronic cir cuits to one in which digital processing systems, in a combined hardware-software configura. tion, are quite capable of implementing advanced mathematical and statistical procedures. Moreover, as these processing techniques become more sophisticated and powerful, the sharper resolution of the resulting system brings into question the classic distributional assumptions of Gaussianity for both noise and signal processes. This in turn opens the door to a fundamental reexamination of structure and inference methods for non-Gaussian sto chastic processes together with the application of such processes as models in the context of filtering, estimation, detection and signal extraction. Based on the premise that such a fun damental reexamination was timely, in 1981 the Office of Naval Research initiated a research effort in Non-Gaussian Signal Processing under the Selected Research Opportunities Program.
