Detection of Multiple Change Points from Clustering Individual Observations

By Joe H. Sullivan. 2002

64 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/835dd64619e1df3bb89a233c40aad1ce5c330203

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Jeffrey B. Birch
2. James D. Williams
3. William H. Woodall
4. Guido Masarotto
5. Giovanna Capizzi
6. Changliang Zou

In Statistics and Probability

1. William H. Woodall
2. Jeffrey B. Birch
3. Guido Masarotto
4. Giovanna Capizzi
5. James D. Williams
6. Changliang Zou
7. Fugee Tsung
8. Peihua Qiu

Citations in the corpus

• Phase I Distribution-Free Analysis of Univariate Data (2013, methodology): “…of commonly employed Phase I control charts, such the Shewharttype (Shewhart (1939)) and the CUSUM-type (Page (1955)) control charts or the charts based on binary and multiple segmentation (Sullivan and Woodall (1996), Sullivan (2002)) are exact only if the normality assumption is satisfied.”
• Statistical monitoring of nonlinear product and process quality profiles (2007, methodology): “D,i was effective in detecting both a step and ramp shift in the mean vector during Phase I. Sullivan and Woodall [18] also showed that the T 2 D values are invariant to a full-rank linear transformation on the observations.”