Thomas K. Philips, Emmanuel Yashchin, et al.
Journal of Portfolio Management
Chemoff’s bound on P[X ≥ t] is used almost universally when a tight bound on tail probabilities is required. In this article we show that for all positive t and for all distributions, the moment bound is tighter than Chemoff’s bound. By way of example, we demonstrate that the improvement is often substantial. © Taylor & Francis Group, LLC.
Thomas K. Philips, Emmanuel Yashchin, et al.
Journal of Portfolio Management
Cheng-Shang Chang, Randolph Nelson
Communications in Statistics. Stochastic Models
Randolph Nelson, Donald Towsley
Journal of the ACM (JACM)
Cheng-Shang Chang, Randolph Nelson, et al.
Performance Evaluation