We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Bayesian sequential monitoring of density estimates.
- Authors
Shamp, Wright; Linero, Antonio; Chicken, Eric
- Abstract
In this paper, we consider sequentially estimating the density of univariate data. We utilize Pólya trees to develop a statistical process control (SPC) methodology. Our proposed methodology monitors the distribution of the sequentially observed data and detects when the generating density differs from an in‐control standard. We also propose an approximation that merges the probability mass of multiple possible changepoints to curb computational complexity while maintaining the accuracy of the monitoring procedure. We show in simulation experiments that our approach is capable of quickly detecting when a changepoint has occurred while controlling the number of false alarms, and performs well relative to competing methods. We then use our methodology to detect changepoints in high‐frequency foreign exchange (Forex) return data.
- Subjects
STATISTICAL process control; DENSITY; FALSE alarms; COMPUTATIONAL complexity
- Publication
Quality & Reliability Engineering International, 2022, Vol 38, Issue 4, p1826
- ISSN
0748-8017
- Publication type
Article
- DOI
10.1002/qre.3050