Focusing on the behavior of extreme values, this book provides a clear and efficient exploration of essential mathematical and stochastic process techniques. It emphasizes the study of phenomena rooted in independent and identically distributed random variables and vectors, making complex concepts accessible for readers interested in advanced statistical methods and their applications.
Designed for graduate students across various disciplines, this advanced primer focuses on the principles of probability and their practical applications in research projects. It serves as a vital resource for those in biology, engineering, finance, and more, ensuring a comprehensive understanding of probability concepts relevant to fields beyond mathematics.
Focusing on heavy-tail analysis, this text offers a comprehensive blend of mathematical, probabilistic, and statistical tools essential for understanding and modeling heavy-tailed distributions. It emphasizes probability modeling alongside statistical methods for fitting these models, making it a valuable resource for researchers and practitioners interested in this specialized area of statistics.
Focusing on stochastic processes, this textbook offers an engaging and accessible approach to modeling time-varying randomness. It features numerous examples, exercises, and computational procedures, making it suitable for students across various applied sciences. The content is modularized, clearly distinguishing between rigorous proofs and plausibility arguments, catering to both beginners and those with a strong background. Designed for readers with a graduate-level understanding of pre-measure theoretic probability, it avoids advanced conditioning concepts until later chapters.