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Parameter Estimation in Sto...
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
Monografía
monografia Rebiun12157215 https://catalogo.rebiun.org/rebiun/record/Rebiun12157215 100301s2008 gw | s |||| 0|eng d 9783540744481 978-3-540-74448-1 9783540744474 ed. impresa) UPNA0460409 UPM 991002166239704212 UCAR 991002736779704213 CBUC 991053902379706706 UPVA 990003395930203706 UMA.RE Bishwal, Jaya P. N. Parameter Estimation in Stochastic Differential Equations Recurso electrónico] by Jaya P. N. Bishwal Servicio en línea Berlin, Heidelberg Springer Berlin Heidelberg 2008 Berlin, Heidelberg Berlin, Heidelberg Springer Berlin Heidelberg digital Lecture Notes in Mathematics 0075-8434 1923 Preface -- 1.Parametric Stochastic Differential Equations -- Part I: Continuous Sampling -- 2.Rates of Weak Convergence of Estimators in Homogeneous Diffusions. -3.Large Deviations for Estimators in Homogeneous Diffusions -- 4.Local Asymptotic Mixed Normality for Nonhomogeneous Diffusions -- 5.Bayes and Sequential Estimation in Stochastic PDEs -- 6.Maximum Likelihood Estimation in Fractional Diffusions -- Part II: Discrete Sampling -- 7.Approximate Maximum Likelihood Estimation in Nonhomogeneous Diffusions -- 8.Rates of Weak Convergence of Estimators in the Ornstein-Uhlenbeck Process -- 9.Local Asymptotic Normality for Discretely Observed Homogeneous Diffusions -- 10.Estimating Functions for Discretely Observed Homogeneous Diffusions -- Bibliography -- Index Acceso restringido a miembros del Consorcio de Bibliotecas Universitarias de Andalucía 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 Modo de acceso: World Wide Web Springer Mathematics Finance Numerical analysis Distribution (Probability theory) Mathematical statistics Mathematics Probability Theory and Stochastic Processes Statistical Theory and Methods Quantitative Finance Numerical Analysis Game Theory, Economics, Social and Behav. Sciences SpringerLink (Online service) SpringerLink eBooks (Servicio en línea) Lecture Notes in Mathematics (Servicio en línea) Springer eBooks Springer eBooks