LEADER 03542nam 2200589 450 001 9910484642803321 005 20220223101727.0 010 $a3-540-74448-7 024 7 $a10.1007/978-3-540-74448-1 035 $a(CKB)1000000000437249 035 $a(SSID)ssj0000319339 035 $a(PQKBManifestationID)11256973 035 $a(PQKBTitleCode)TC0000319339 035 $a(PQKBWorkID)10338516 035 $a(PQKB)11325031 035 $a(DE-He213)978-3-540-74448-1 035 $a(MiAaPQ)EBC3062956 035 $a(MiAaPQ)EBC6857793 035 $a(Au-PeEL)EBL6857793 035 $a(PPN)123728495 035 $a(EXLCZ)991000000000437249 100 $a20220223d2007 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aParameter estimation in stochastic differential equations /$fJaya P. N. Bishwal 205 $a1st ed. 2008. 210 1$aBerlin :$cSpringer,$d[2007] 210 4$dİ2007 215 $a1 online resource (XIV, 268 p.) 225 1 $aLecture Notes in Mathematics ;$v1923 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-74447-9 320 $aIncludes bibliographical references and index. 327 $aContinuous 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. 330 $aParameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions. The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods. Useful because of the current availability of high frequency data is the study of refined asymptotic properties of several estimators when the observation time length is large and the observation time interval is small. Also space time white noise driven models, useful for spatial data, and more sophisticated non-Markovian and non-semimartingale models like fractional diffusions that model the long memory phenomena are examined in this volume. 410 0$aLecture notes in mathematics (Springer-Verlag) ;$v1923. 606 $aParameter estimation 606 $aStochastic differential equations$xStatistical methods 615 0$aParameter estimation. 615 0$aStochastic differential equations$xStatistical methods. 676 $a519.544 700 $aBishwal$b Jaya P. N.$0472516 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484642803321 996 $aParameter estimation in stochastic differential equations$9230593 997 $aUNINA