03540nam 2200589 450 99646651360331620220223101727.03-540-74448-710.1007/978-3-540-74448-1(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.472516MiAaPQMiAaPQMiAaPQBOOK996466513603316Parameter estimation in stochastic differential equations230593UNISA01592nam0 22003853i 450 RMS120392720251003044345.000711219350071230106international edition007366009420091207d2003 ||||0itac50 baengusz01i xxxe z01nElectric machineryA. E. Fitzgerald, Charles Kingsley Jr., Stephen D. Umans 6. edBostonMcGraw-Hill©2003XV, 688 p.ill.23 cm.McGraw-Hill series in electrical and computer engineering. Power and energy001MIL03052382001 McGraw-Hill series in electrical and computer engineering. Power and energyMacchine elettricheFIRCFIC009298E621.31Generazione, modificazione, accumulazione, trasmissione dell'energia elettrica14621.31042Energia elettrica. Macchine e impianti elettrici22Fitzgerald, Arthur EugeneSBLV174242070779Umans, Stephen D.UBOV003275070305407Kingsley, Charles <1904- >UFIV028606070771677ITIT-00000020091207IT-BN0095 NAP 01SALA DING $RMS1203927Biblioteca Centralizzata di Ateneo1 v. 01SALA DING 621.31 FIT.el 0102 0000075635 VMA A4 1 v.Y 2009120320091203 01Electric machinery1574870UNISANNIO