Parameter estimation in stochastic differential equations /
Bishwal, Jaya P. N.
Parameter estimation in stochastic differential equations / Jaya P.N. Bishwal. - Berlin : Springer, ©2008. - 1 online resource (xi, 264 pages) - Lecture notes in mathematics, 1923 0075-8434 ; . - Lecture notes in mathematics (Springer-Verlag) ; 1923. .
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.
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.
English.
9783540744481 3540744487 3540744479 9783540744474
10.1007/978-3-540-74448-1 doi
978-3-540-74447-4 Springer http://www.springerlink.com
985961147 DE-101
Parameter estimation.
Stochastic differential equations.
Estimation d'un paramètre.
Équations différentielles stochastiques.
Estimación estadística
Ecuaciones diferenciales
Ecuaciones diferenciales estocásticas
Parameter estimation
Stochastic differential equations
QA276.8 / .B57 2008eb
519.544
Parameter estimation in stochastic differential equations / Jaya P.N. Bishwal. - Berlin : Springer, ©2008. - 1 online resource (xi, 264 pages) - Lecture notes in mathematics, 1923 0075-8434 ; . - Lecture notes in mathematics (Springer-Verlag) ; 1923. .
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.
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.
English.
9783540744481 3540744487 3540744479 9783540744474
10.1007/978-3-540-74448-1 doi
978-3-540-74447-4 Springer http://www.springerlink.com
985961147 DE-101
Parameter estimation.
Stochastic differential equations.
Estimation d'un paramètre.
Équations différentielles stochastiques.
Estimación estadística
Ecuaciones diferenciales
Ecuaciones diferenciales estocásticas
Parameter estimation
Stochastic differential equations
QA276.8 / .B57 2008eb
519.544