TY  - BOOK
AU  - Bishwal,Jaya P.N.
TI  - Parameter estimation in stochastic differential equations
T2  - Lecture notes in mathematics,
SN  - 9783540744481
AV  - QA276.8 .B57 2008eb
U1  - 519.544 22
PY  - 2008///
CY  - Berlin
PB  - Springer
KW  - Parameter estimation
KW  - Stochastic differential equations
KW  - Estimation d'un paramètre
KW  - Équations différentielles stochastiques
KW  - Estimación estadística
KW  - embne
KW  - Ecuaciones diferenciales
KW  - Ecuaciones diferenciales estocásticas
KW  - embucm
KW  - fast
N1  - Includes bibliographical references and index; Rates of weak convergence of estimators in homogeneous diffusions -- Large deviations of estimators in homogeneous diffusions -- Local asymptotic mixed normality for nonhomogeneous diffusions -- Bayes and sequential estimation in stochastic PDEs -- Maximum likelihood estimation in fractional diffusions -- Approximate maximum likelihood estimation in nonhomogeneous diffusions -- Rates of weak convergence estimators in the Ornstein-Uhlenbeck process -- Local asymptotic normality for discretely observed homogeneous diffusions -- Estimations function for discretely observed homogeneous diffusions
N2  - 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
UR  - https://link-springer-com.libraryproxy.ist.ac.at/10.1007/978-3-540-74448-1
ER  -