Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions. The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods. Useful because of the current availability of high frequency data is the study of refined asymptotic properties of several estimators when the observation time length is large and the observation time interval is small. Also space time white noise driven models, useful for spatial data, and more sophisticated non-Markovian and non-semimartingale models like fractional diffusions that model the long memory phenomena are examined in this volume.
Lecture Notes in Mathematics Editors: J.-M. Morel, Cachan F. Takens, Groningen B. Teissier, Paris
1923
Jaya P.N. Bishwal
Parameter Estimation in Stochastic Differential Equations
ABC
Author Jaya P.N. Bishwal Department of Mathematics and Statistics University of North Carolina at Charlotte 376 Fretwell Bldg. 9201 University City Blvd. Charlotte NC 28223-0001 USA e-mail:
[email protected] URL: http://www.math.uncc.edu/∼jpbishwa
Library of Congress Control Number: 2007933500 Mathematics Subject Classification (2000): 60H10, 60H15, 60J60, 62M05, 62M09 ISSN print edition: 0075-8434 ISSN electronic edition: 1617-9692 ISBN 978-3-540-74447-4 Springer Berlin Heidelberg New York DOI 10.1007/978-