Gaussian and Non-Gaussian Linear Time Series and Random Fields PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Gaussian and Non-Gaussian Linear Time Series and Random Fields PDF full book. Access full book title Gaussian and Non-Gaussian Linear Time Series and Random Fields by Murray Rosenblatt. Download full books in PDF and EPUB format.
Author: Murray Rosenblatt
Publisher: Springer Science & Business Media
ISBN: 1461212626
Category : Mathematics
Languages : en
Pages : 252
Get Book Here
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.
Author: Murray Rosenblatt
Publisher: Springer Science & Business Media
ISBN: 1461212626
Category : Mathematics
Languages : en
Pages : 252
Get Book Here
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.
Author: Murray Rosenblatt
Publisher: Springer Science & Business Media
ISBN: 9780387989174
Category : Mathematics
Languages : en
Pages : 272
Get Book Here
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.
Author: Murray Rosenblatt
Publisher: Springer Science & Business Media
ISBN: 1461251567
Category : Mathematics
Languages : en
Pages : 253
Get Book Here
Book Description
This book has a dual purpose. One of these is to present material which selec tively will be appropriate for a quarter or semester course in time series analysis and which will cover both the finite parameter and spectral approach. The second object is the presentation of topics of current research interest and some open questions. I mention these now. In particular, there is a discussion in Chapter III of the types of limit theorems that will imply asymptotic nor mality for covariance estimates and smoothings of the periodogram. This dis cussion allows one to get results on the asymptotic distribution of finite para meter estimates that are broader than those usually given in the literature in Chapter IV. A derivation of the asymptotic distribution for spectral (second order) estimates is given under an assumption of strong mixing in Chapter V. A discussion of higher order cumulant spectra and their large sample properties under appropriate moment conditions follows in Chapter VI. Probability density, conditional probability density and regression estimates are considered in Chapter VII under conditions of short range dependence. Chapter VIII deals with a number of topics. At first estimates for the structure function of a large class of non-Gaussian linear processes are constructed. One can determine much more about this structure or transfer function in the non-Gaussian case than one can for Gaussian processes. In particular, one can determine almost all the phase information.
Author: N. Balakrishna
Publisher: Springer Nature
ISBN: 9811681627
Category : Mathematics
Languages : en
Pages : 238
Get Book Here
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.
Author: Frantisek Stulajter
Publisher: Springer Science & Business Media
ISBN: 1475736290
Category : Mathematics
Languages : en
Pages : 237
Get Book Here
Book Description
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.
Author: Paul W. Jr. Mielke
Publisher: Springer Science & Business Media
ISBN: 1475734492
Category : Mathematics
Languages : en
Pages : 359
Get Book Here
Book Description
The book provides a comprehensive treatment of statistical inference using permutation techniques. It features a variety of useful and powerful data analytic tools that rely on very few distributional assumptions. Although many of these procedures have appeared in journal articles, they are not readily available to practitioners.
Author: D.A. Sprott
Publisher: Springer Science & Business Media
ISBN: 0387227660
Category : Mathematics
Languages : en
Pages : 254
Get Book Here
Book Description
A treatment of the problems of inference associated with experiments in science, with the emphasis on techniques for dividing the sample information into various parts, such that the diverse problems of inference that arise from repeatable experiments may be addressed. A particularly valuable feature is the large number of practical examples, many of which use data taken from experiments published in various scientific journals. This book evolved from the authors own courses on statistical inference, and assumes an introductory course in probability, including the calculation and manipulation of probability functions and density functions, transformation of variables and the use of Jacobians. While this is a suitable text book for advanced undergraduate, Masters, and Ph.D. statistics students, it may also be used as a reference book.
Author: Anthony Atkinson
Publisher: Springer Science & Business Media
ISBN: 1461211603
Category : Mathematics
Languages : en
Pages : 342
Get Book Here
Book Description
Graphs are used to understand the relationship between a regression model and the data to which it is fitted. The authors develop new, highly informative graphs for the analysis of regression data and for the detection of model inadequacies. As well as illustrating new procedures, the authors develop the theory of the models used, particularly for generalized linear models. The book provides statisticians and scientists with a new set of tools for data analysis. Software to produce the plots is available on the authors website.
Author: Edward Valachovic
Publisher: Cambridge Scholars Publishing
ISBN: 1527564169
Category : Mathematics
Languages : en
Pages : 375
Get Book Here
Book Description
The book introduces valuable new data analysis methods in time and space, and provides many examples and recommendations for new developments. It will teach the reader how to use powerful, but very flexible, tools, frequently referred to as Kolmogorov-Zurbenko Filters. The main construction of these tools is derived from spectral concepts where natural laws occur. Rather than forcing models on data, they allow us to discover the nature of phenomena hidden within the data. The methods outlined here are capable of obtaining accurate results within very noisy environments. Their extremely accurate spectral diagnostics permits the separation of different sources of influences within the data. Treating each source separately can achieve highly accurate explanations of the total picture. For example, this approach is able to identify the most dangerous moments and locations for hurricanes and tornados.
Author: Paul Fieldhouse
Publisher: Nelson Thornes
ISBN: 9780748737239
Category :
Languages : en
Pages : 750
Get Book Here
Book Description
