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Simulation And Inference For Stochastic Processes With Yuima: A Comprehensive R Framework For Sdes And Other Stochastic Processe
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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, Lvy 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, Leadlag 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.
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- Q: What is the page count of this book? A: This book has two hundred eighty-one pages. It provides a comprehensive overview of stochastic processes and their simulations.
- Q: What are the dimensions of this book? A: The book measures six point one inches in length, zero point six four inches in width, and nine point two five inches in height.
- Q: What type of binding does this book have? A: This book is published in a paperback binding. It is designed for durability and ease of handling.
- Q: How do I use the YUIMA package? A: To use the YUIMA package, you can download it from CRAN and follow the examples provided in the book for simulations and analyses.
- Q: Is this book suitable for beginners in statistics? A: Yes, this book is suitable for beginners. It explains foundational concepts in statistics and stochastic processes before diving into complex applications.
- Q: Can I apply the techniques in this book to real-world data? A: Yes, the techniques outlined in this book can be applied to real-world data. It includes simulation experiments and applications across various fields.
- Q: How should I store this book to maintain its condition? A: Store this book in a cool, dry place to maintain its condition. Avoid direct sunlight and moisture to prevent damage.
- Q: Is there a warranty for this book? A: No, there is no warranty for books. However, you can check the return policy if you receive a damaged copy.
- Q: How do I clean the cover of this book? A: To clean the cover, gently wipe it with a soft, dry cloth. Avoid using cleaning solutions that could damage the material.
- Q: What are the main topics covered in this book? A: This book covers simulation and inference for stochastic processes, including topics like stochastic differential equations and statistical analyses.
- Q: Is this book recommended for advanced users? A: Yes, advanced users will also find this book valuable. It delves into complex statistical methods and real-world applications.
- Q: What is the author’s background? A: The author, Stefano M. Iacus, is an expert in statistical modeling and stochastic processes, making this book a credible resource.
- Q: Are there any prerequisites for reading this book? A: A basic understanding of statistics and R programming is recommended, but the book is structured to aid learning.
- Q: Can I find examples of stochastic processes in this book? A: Yes, the book includes numerous examples of stochastic processes, demonstrating their applications in various fields.
- Q: What is the target audience for this book? A: The target audience includes students, researchers, and professionals in statistics and related fields.