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020 _a9783540744481
_9978-3-540-74448-1
024 7 _a10.1007/978-3-540-74448-1
_2doi
050 4 _aQA299.6-433
072 7 _aPBK
_2bicssc
072 7 _aMAT034000
_2bisacsh
072 7 _aPBK
_2thema
082 0 4 _a515
_223
100 1 _aBishwal, Jaya P. N.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aParameter Estimation in Stochastic Differential Equations
_h[electronic resource] /
_cby Jaya P. N. Bishwal.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2008.
300 _aXIV, 268 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Mathematics,
_x0075-8434 ;
_v1923
505 0 _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.
520 _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.
650 0 _aGlobal analysis (Mathematics).
650 0 _aDistribution (Probability theory.
650 0 _aFinance.
650 0 _aMathematical statistics.
650 0 _aNumerical analysis.
650 0 _aMathematics.
650 1 4 _aAnalysis.
_0http://scigraph.springernature.com/things/product-market-codes/M12007
650 2 4 _aProbability Theory and Stochastic Processes.
_0http://scigraph.springernature.com/things/product-market-codes/M27004
650 2 4 _aQuantitative Finance.
_0http://scigraph.springernature.com/things/product-market-codes/M13062
650 2 4 _aStatistical Theory and Methods.
_0http://scigraph.springernature.com/things/product-market-codes/S11001
650 2 4 _aNumerical Analysis.
_0http://scigraph.springernature.com/things/product-market-codes/M14050
650 2 4 _aGame Theory, Economics, Social and Behav. Sciences.
_0http://scigraph.springernature.com/things/product-market-codes/M13011
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540842767
776 0 8 _iPrinted edition:
_z9783540744474
830 0 _aLecture Notes in Mathematics,
_x0075-8434 ;
_v1923
856 4 0 _uhttps://doi.org/10.1007/978-3-540-74448-1
912 _aZDB-2-SMA
912 _aZDB-2-LNM
999 _c10445
_d10445