Statistical Inference For Ergodic Diffusion Processes
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Statistical Inference For Ergodic Diffusion Processes
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Author : Yury A. Kutoyants
language : en
Publisher:
Release Date : 2014-01-15
Statistical Inference For Ergodic Diffusion Processes written by Yury A. Kutoyants and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Statistical Inference For Ergodic Diffusion Processes
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Author : Yu. A. Kutoyants
language : en
Publisher: Springer Science & Business Media
Release Date : 2004
Statistical Inference For Ergodic Diffusion Processes written by Yu. A. Kutoyants and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Mathematics categories.
The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theoretical results from over ten years of mathematical literature and presents, for the first time in book form, many new techniques and approaches.
Statistical Inference In Financial And Insurance Mathematics With R
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Author : Alexandre Brouste
language : en
Publisher: Elsevier
Release Date : 2017-11-22
Statistical Inference In Financial And Insurance Mathematics With R written by Alexandre Brouste and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-22 with Mathematics categories.
Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables are frequent and well understood, especially those consisting of probability measures of an exponential type. However, the aforementioned applications also offer non-classical experiments implying observation samples of independent but not identically distributed random variables or even dependent random variables. Three examples of such experiments are treated in this book. First, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments with Markov chains are considered next. Finally, various statistical experiments generated by fractional Gaussian noise are also described. In this book, asymptotic properties of several sequences of estimators are detailed. The notion of asymptotical efficiency is discussed for the different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software are given throughout the text. - Examines a range of statistical inference methods in the context of finance and insurance applications - Presents the LAN (local asymptotic normality) property of likelihoods - Combines the proofs of LAN property for different statistical experiments that appears in financial and insurance mathematics - Provides the proper description of such statistical experiments and invites readers to seek optimal estimators (performed in R) for such statistical experiments
Simulation And Inference For Stochastic Differential Equations
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Author : Stefano M. Iacus
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-04-27
Simulation And Inference For Stochastic Differential Equations written by Stefano M. Iacus and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-27 with Computers categories.
This book covers a highly relevant and timely topic that is of wide interest, especially in finance, engineering and computational biology. The introductory material on simulation and stochastic differential equation is very accessible and will prove popular with many readers. While there are several recent texts available that cover stochastic differential equations, the concentration here on inference makes this book stand out. No other direct competitors are known to date. With an emphasis on the practical implementation of the simulation and estimation methods presented, the text will be useful to practitioners and students with minimal mathematical background. What’s more, because of the many R programs, the information here is appropriate for many mathematically well educated practitioners, too.
Statistical Models And Methods For Reliability And Survival Analysis
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Author : Vincent Couallier
language : en
Publisher: John Wiley & Sons
Release Date : 2013-12-31
Statistical Models And Methods For Reliability And Survival Analysis written by Vincent Couallier and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-31 with Mathematics categories.
Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical Models and Methods in Survival Analysis, and Reliability and Maintenance. The book is intended for researchers interested in statistical methodology and models useful in survival analysis, system reliability and statistical testing for censored and non-censored data.
Statistical Methods For Stochastic Differential Equations
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Author : Mathieu Kessler
language : en
Publisher: CRC Press
Release Date : 2012-05-17
Statistical Methods For Stochastic Differential Equations written by Mathieu Kessler and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-17 with Mathematics categories.
The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations. Written to be accessible to both new students and seasoned researchers, each self-contained chapter starts with introductions to th
Inference And Prediction In Large Dimensions
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Author : Denis Bosq
language : en
Publisher: John Wiley & Sons
Release Date : 2008-03-11
Inference And Prediction In Large Dimensions written by Denis Bosq and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-11 with Mathematics categories.
This book offers a predominantly theoretical coverage of statistical prediction, with some potential applications discussed, when data and/ or parameters belong to a large or infinite dimensional space. It develops the theory of statistical prediction, non-parametric estimation by adaptive projection – with applications to tests of fit and prediction, and theory of linear processes in function spaces with applications to prediction of continuous time processes. This work is in the Wiley-Dunod Series co-published between Dunod (www.dunod.com) and John Wiley and Sons, Ltd.
Simulation And Inference For Stochastic Processes With Yuima
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Author : Stefano M. Iacus
language : en
Publisher: Springer
Release Date : 2018-06-01
Simulation And Inference For Stochastic Processes With Yuima written by Stefano M. Iacus and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-01 with Computers categories.
The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA, COGARCH, and Point processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on. YUIMA also supports stochastic numerical analysis by fast computation of the expected value of functionals of stochastic processes through automatic asymptotic expansion by means of the Malliavin calculus. All models can be multidimensional, multiparametric or non parametric.The book explains briefly the underlying theory for simulation and inference of several classes of stochastic processes and then presents both simulation experiments and applications to real data. Although these processes have been originally proposed in physics and more recently in finance, they are becoming popular also in biology due to the fact the time course experimental data are now available. The YUIMA package, available on CRAN, can be freely downloaded and this companion book will make the user able to start his or her analysis from the first page.
Parameter Estimation In Stochastic Volatility Models
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Author : Jaya P. N. Bishwal
language : en
Publisher: Springer Nature
Release Date : 2022-08-06
Parameter Estimation In Stochastic Volatility Models written by Jaya P. N. Bishwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-06 with Mathematics categories.
This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.
Statistical Models And Methods For Biomedical And Technical Systems
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Author : Filia Vonta
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-05
Statistical Models And Methods For Biomedical And Technical Systems written by Filia Vonta and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-05 with Medical categories.
This book deals with the mathematical aspects of survival analysis and reliability as well as other topics, reflecting recent developments in the following areas: applications in epidemiology; probabilistic and statistical models and methods in reliability; models and methods in survival analysis, longevity, aging, and degradation; accelerated life models; quality of life; new statistical challenges in genomics. The work will be useful to a broad interdisciplinary readership of researchers and practitioners in applied probability and statistics, industrial statistics, biomedicine, biostatistics, and engineering.