Study of a Memory Type Shrinkage Estimator of Population Mean in Quality Control Process

Abstract

Controlling and improving quality is a major business strategy for various organizations. A company gains an advantage over its competitors by maintaining a high level of quality for products or services and pleasing its customers or clients. Control charts are a tool used in Statistical Process Control for the purpose of reducing variability in a process as well as estimating certain parameters. In the current manuscript, auxiliary information has been utilized to propose an estimator for process mean using hybrid exponentially moving average statistic. The proposed memory-type estimator utilizes prior information to provide better estimates from data which is time-scale based than conventional estimators which were designed for data collected at a single point of time. The properties of the estimator have been studied under Simple Random Sampling as well as Stratified Sampling structure. Simulation study has been conducted to illustrate its efficiency over contemporary estimators. Application to real data on datasets “Machine”, “Automobile” and “Auto MPG” is introduced to illustrate the method.

Keywords

• Control chart
• estimation
• hybrid exponentially weighted moving average
• population mean
• quality control
• sample survey