Computational Statistics & Data Analysis
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Adaptive CUSUM procedures with Markovian mean estimation
Computational Statistics & Data Analysis
Control chart based on likelihood ratio for monitoring linear profiles
Computational Statistics & Data Analysis
Adaptive CUSUM control chart with variable sampling intervals
Computational Statistics & Data Analysis
New EWMA control charts for monitoring process dispersion
Computational Statistics & Data Analysis
Evaluation of exponentially weighted moving variance control chart subject to linear drifts
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Control charts based on generalized likelihood ratio test (GLRT) are attractive from both theoretical and practical points of view. Most of the existing works in the literature focusing on the detection of the process mean and variance are almost based on the assumption that the shifts remain constant over time. The case of the patterned mean and variance changes may not be well discussed. In this research, we propose a new control chart which integrates the exponentially weighted moving average (EWMA) procedure with the GLRT statistics to monitor the process with patterned mean and variance shifts. The attractive advantage of our control chart is its reference-free property. Due to the good properties of GLRT and EWMA procedures, our simulation results show that the proposed chart provides quite effective and robust detecting ability for various types of shifts. The implementation of our proposed control chart is illustrated by a real data example from chemical process control.