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010   $a9783540744481
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024 7 $a10.1007/978-3-540-74448-1
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100   $a20100301d2008      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aParameter Estimation in Stochastic Differential Equations /$fby Jaya P. N. Bishwal
205   $a1st ed. 2008.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2008.
215   $a1 online resource (XIV, 268 p.) 
225 1 $aLecture Notes in Mathematics,$x1617-9692 ;$v1923
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$a9783540744474 
311 08$a3540744479 
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,$x1617-9692 ;$v1923
606   $aMathematical analysis
606   $aProbabilities
606   $aSocial sciences$xMathematics
606   $aStatistics
606   $aNumerical analysis
606   $aGame theory
606   $aAnalysis
606   $aProbability Theory
606   $aMathematics in Business, Economics and Finance
606   $aStatistical Theory and Methods
606   $aNumerical Analysis
606   $aGame Theory
615  0$aMathematical analysis.
615  0$aProbabilities.
615  0$aSocial sciences$xMathematics.
615  0$aStatistics.
615  0$aNumerical analysis.
615  0$aGame theory.
615 14$aAnalysis.
615 24$aProbability Theory.
615 24$aMathematics in Business, Economics and Finance.
615 24$aStatistical Theory and Methods.
615 24$aNumerical Analysis.
615 24$aGame Theory.
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