03542nam 2200589 450 991048464280332120220223101727.03-540-74448-710.1007/978-3-540-74448-1(CKB)1000000000437249(SSID)ssj0000319339(PQKBManifestationID)11256973(PQKBTitleCode)TC0000319339(PQKBWorkID)10338516(PQKB)11325031(DE-He213)978-3-540-74448-1(MiAaPQ)EBC3062956(MiAaPQ)EBC6857793(Au-PeEL)EBL6857793(PPN)123728495(EXLCZ)99100000000043724920220223d2007 uy 0engurnn|008mamaatxtccrParameter estimation in stochastic differential equations /Jaya P. N. Bishwal1st ed. 2008.Berlin :Springer,[2007]©20071 online resource (XIV, 268 p.) Lecture Notes in Mathematics ;1923Bibliographic Level Mode of Issuance: Monograph3-540-74447-9 Includes bibliographical references and index.Continuous Sampling -- Parametric Stochastic Differential Equations -- 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 -- Discrete Sampling -- Approximate Maximum Likelihood Estimation in Nonhomogeneous Diffusions -- Rates of Weak Convergence of Estimators in the Ornstein-Uhlenbeck Process -- Local Asymptotic Normality for Discretely Observed Homogeneous Diffusions -- Estimating 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.Lecture notes in mathematics (Springer-Verlag) ;1923.Parameter estimationStochastic differential equationsStatistical methodsParameter estimation.Stochastic differential equationsStatistical methods.519.544Bishwal Jaya P. N.472516MiAaPQMiAaPQMiAaPQBOOK9910484642803321Parameter estimation in stochastic differential equations230593UNINA