Stochastic Processes with R: An Introduction [Kõva köide]

"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"--

Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes.