000 | 04086nam a22005655i 4500 | ||
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001 | 978-3-540-74448-1 | ||
003 | DE-He213 | ||
005 | 20190213151354.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2008 gw | s |||| 0|eng d | ||
020 |
_a9783540744481 _9978-3-540-74448-1 |
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024 | 7 |
_a10.1007/978-3-540-74448-1 _2doi |
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050 | 4 | _aQA299.6-433 | |
072 | 7 |
_aPBK _2bicssc |
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072 | 7 |
_aMAT034000 _2bisacsh |
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_aPBK _2thema |
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082 | 0 | 4 |
_a515 _223 |
100 | 1 |
_aBishwal, Jaya P. N. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIV, 268 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 | ||
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