Control Charts Based on the Exponential Distribution: Adapting Runs Rules for the t Chart

By Eduardo De Santiago, Joel Smith. 2013

148 citations recorded by the source corpus.

Subfields: Medical Laboratory Technology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4484e1fcba9b9bdd4a8f762e5204e81ee6800d13

How researchers see this paper

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

In Medical Laboratory Technology

1. Douglas C. Montgomery
2. Muhammad Aslam
3. Nasrullah Khan
4. Muhammad Azam
5. William H. Woodall
6. Muhammad Naveed

In Statistics and Probability

1. Douglas C. Montgomery
2. William H. Woodall
3. Muhammad Aslam
4. Nasrullah Khan
5. Muhammad Azam
6. Muhammad Naveed
7. Changliang Zou
8. Fugee Tsung

Citations in the corpus

• Some Current Directions in the Theory and Application of Statistical Process Monitoring (2014, background): “Santiago and Smith (2013) showed, however, that this approach leads to surprisingly poor out-of-control performance in detecting decreases in the average time between events with out-of-control average run lengths (ARLs) far exceeding the in-control ARL.”
• Design of a Control Chart Using Extended EWMA Statistic (2018, methodology): “Here, we made a comparison of both charts by using application data taken from Santiago and Smith [9].”