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Record Nr. |
UNINA9910484642803321 |
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Autore |
Bishwal Jaya P. N. |
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Titolo |
Parameter Estimation in Stochastic Differential Equations / / by Jaya P. N. Bishwal |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2008 |
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ISBN |
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Edizione |
[1st ed. 2008.] |
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Descrizione fisica |
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1 online resource (XIV, 268 p.) |
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Collana |
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Lecture Notes in Mathematics, , 1617-9692 ; ; 1923 |
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Disciplina |
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Soggetti |
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Mathematical analysis |
Probabilities |
Social sciences - Mathematics |
Statistics |
Numerical analysis |
Game theory |
Analysis |
Probability Theory |
Mathematics in Business, Economics and Finance |
Statistical Theory and Methods |
Numerical Analysis |
Game Theory |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 |
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Observed Homogeneous Diffusions -- Estimating Function for Discretely Observed Homogeneous Diffusions. |
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Sommario/riassunto |
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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. |
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