Stochastic Processes with R An Introduction
Stochastic Processes with R: An Introduction by Olga Korosteleva, published by CRC Press in 2022, offers a balanced approach to the study of stochastic processes. This 190-page book is designed for readers who have a foundational understanding of calculus-based probability theory and statistics. It emphasizes practical applications over extensive theoretical discussions, making it suitable for statistics majors who seek to engage with simulated trajectories and real-world data analysis.
Readers will find that this book presents various methods for estimating parameters of stochastic processes, including linear regression and maximum likelihood estimators. The content is tailored to those in fields such as mathematics, physics, and engineering, while intentionally avoiding complex topics like sigma algebra and Ito calculus. This edition serves as a resource for understanding stochastic processes through a more application-focused lens, providing insights that are relevant for both academic and practical contexts.
Official synopsis Publisher
“The academic level of this book is not too elementary yet not too advanced. It is assumed that the reader has taken calculus-based probability theory and statistics. Not a whole lot of statistical analysis is present in this book. In applications there are some attempts to estimate parameters of stochastic processes via linear regression, maximum likelihood and method of moments estimators. Typically, a course on stochastic processes is taught to pure mathematics, applied mathematics, physics, and engineering majors, and the selection of processes and level of exposition differ. Most of the books involved sigma algebra, martingales, and Ito calculus, which I deliberately not mention in my book. My book is written for statistics majors who benefit from seeing less theory but more simulated trajectories and serious applications, possibly with data analysis involved”–
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