L’ouvrage commence par présenter les graphiques pour séries temporelles offerts par R sur quelques séries. Il fournit ensuite des rappels de statistique mathématique et révise les concepts et...
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L’ouvrage commence par présenter les graphiques pour séries temporelles offerts par R sur quelques séries. Il fournit ensuite des rappels de statistique mathématique et révise les concepts et les modèles classiques de séries. Il présente les structures de séries temporelles dans R et l’importation de telles séries. Il revisite le lissage exponentiel, à la lumière des travaux des 20 dernières années sur la question. Un chapitre est consacré à la simulation de séries. Les méthodes sont rapidement illustrées à l’aide de séries le plus souvent simulées. On étudie ensuite en détail six séries, avec, le plus souvent, la confrontation de plusieurs approches.
Until recently, the most popularly chosen nonparametric methods used symmetric kernel functions to estimate probability density functions of symmetric distributions with unbounded support. Yet many types of economic a...
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ISBN:
(数字)9789811054662
ISBN:
(纸本)9789811054655
Until recently, the most popularly chosen nonparametric methods used symmetric kernel functions to estimate probability density functions of symmetric distributions with unbounded support. Yet many types of economic and financial data are nonnegative and violate the presumed conditions of conventional methods. Examples include incomes, wages, short-term interest rates, and insurance claims. Such observations are often concentrated near the boundary and have long tails with sparse data. Smoothing with asymmetric kernel functions has increasingly gained attention, because the approach successfully addresses the issues arising from distributions that have natural boundaries at the origin and heavy positive skewness. Offering an overview of recently developed kernel methods, complemented by intuitive explanations and mathematical proofs, this book is highly recommended to all readers seeking an in-depth and up-to-date guide to nonparametric estimation methods employing asymmetric kernel smoothing.
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...
详细信息
ISBN:
(数字)9783319555690
ISBN:
(纸本)9783319555676
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 *** 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.
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