Published by CRC Press on 09/29/1988
Book details: 232 pages.
Published by American Mathematical Soc. on 02/19/1971
Book details: 161 pages.
Published by American Mathematical Soc.
Book details: 183 pages.
First derived within the context of life-testing, inverse Gaussian distribution has become one of the most important and widely employed distributions, and is often used to model the lifetimes of components. It is also used as a model in many varied applications, including fatigue analysis, economic prediction analysis, and the analysis of extreme events such as rainfall and flood levels. The interesting features and properties of this distribution make it an important and realistic model in a variety of problems across numerous disciplines. Because of the broad range of applications, this handbook will be useful not only to members of the statistical community but will also appeal to applied scientists, engineers, econometricians, and anyone who desires a thorough evaluation of this important topic.
Published by Routledge on 11/22/2017
Book details: 704 pages.
This book treats the fundamental mathematical properties that hold for a family of Gaussian random variables.
Published by Cambridge University Press on 06/12/1997
Book details: 340 pages.
It is well known that the normal distribution is the most pleasant, one can even say, an exemplary object in the probability theory. It combines almost all conceivable nice properties that a distribution may ever have: symmetry, stability, indecomposability, a regular tail behavior, etc. Gaussian measures (the distributions of Gaussian random functions), as infinite-dimensional analogues of tht
Published by Springer Science & Business Media on 02/28/1995
Book details: 337 pages.
In 1978 the idea of studying the generalized inverse Gaussian distribution was proposed to me by Professor Ole Barndorff-Nielsen, who had come across the distribution in the study of the socalled hyperbolic distributions where it emerged in connection with the representation of the hyperbolic distributions as mixtures of normal distributions. The statistical properties of the generalized inverse Gaussian distribution were at that time virtually unde veloped, but it turned out that the distribution has some nice properties, and models many sets of data satisfactorily. This work contains an account of the statistical properties of the distribu tion as far as they are developed at present. The work was done at the Department of Theoretical Statistics, Aarhus University, mostly in 1979, and was partial fulfilment to wards my M. Sc. degree. I wish to convey my warm thanks to Ole Barn dorff-Nielsen and Preben BI~sild for their advice and for comments on earlier versions of the manuscript and to Jette Hamborg for her skilful typing.
Published by Springer Science & Business Media on 12/06/2012
Book details: 188 pages.
This handbook, now available in paperback, brings together a comprehensive collection of mathematical material in one location. It also offers a variety of new results interpreted in a form that is particularly useful to engineers, scientists, and applied mathematicians. The handbook is not specific to fixed research areas, but rather it has a generic flavor that can be applied by anyone working with probabilistic and stochastic analysis and modeling. Classic results are presented in their final form without derivation or discussion, allowing for much material to be condensed into one volume.
Published by Springer Science & Business Media on 05/24/2007
Book details: 200 pages.
This book examines non-Gaussian distributions. It addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series.
Published by Springer Science & Business Media on 04/05/2007
Book details: 541 pages.
This book is concerned with linear time series and random fields in both the Gaussian and especially the non-Gaussian context focusing on autoregressive moving average models and analogous random fields. The book also deals with problems of prediction and estimation, discussing both the probabilistic and statistical questions that arise in each, Included are notes on the subjects background and history, new results for nonminimum phase non-Gaussian processes, and open questions. The book is intended for users in statistics, mathematic, engineering, the natural sciences, and economics.
Published by Springer Science & Business Media on 02/19/2019
Book details: 246 pages.