Statistical methods for stochastic differential equations
General Material Designation
[Book]
First Statement of Responsibility
/ edited by Mathieu Kessler, Alexander Lindner, Michael S p2 srensen
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Boca Raton, FL
Name of Publisher, Distributor, etc.
: CRC Press,
Date of Publication, Distribution, etc.
, c2012.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
xxiv, 483 p. , ill. , 24 cm.
SERIES
Series Title
(Monographs on statistics and applied probability
Volume Designation
; 124)
NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
Text of Note
Electronic
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
Text of Note
"Preface The chapters of this volume represent the revised versions of the main papers given at the seventh Saeminaire Europaeen de Statistique on "Statistics for Stochastic Differential Equations Models", held at La Manga del Mar Menor, Cartagena, Spain, May 7th-12th, 2007. The aim of the Sلإeminaire Europلإeen de Statistique is to provide talented young researchers with an opportunity to get quickly to the forefront of knowledge and research in areas of statistical science which are of major current interest. As a consequence, this volume is tutorial, following the tradition of the books based on the previous seminars in the series entitled: Networks and Chaos - Statistical and Probabilistic Aspects. Time Series Models in Econometrics, Finance and Other Fields. Stochastic Geometry: Likelihood and Computation. Complex Stochastic Systems. Extreme Values in Finance, Telecommunications and the Environment. Statistics of Spatio-temporal Systems. About 40 young scientists from 15 different nationalities mainly from European countries participated. More than half presented their recent work in short communications; an additional poster session was organized, all contributions being of high quality. The importance of stochastic differential equations as the modeling basis for phenomena ranging from finance to neurosciences has increased dramatically in recent years. Effective and well behaved statistical methods for these models are therefore of great interest. However the mathematical complexity of the involved objects raise theoretical but also computational challenges. The Saeminaire and the present book present recent developments that address, on one hand, properties of the statistical structure of the corresponding models and,"--Provided by publisher.