A bootstrap control chart for inverse Gaussian percentiles

作者：Lio Y L^*; Park Chanseok

来源：Journal of Statistical Computation and Simulation, 2010, 80(3): 287-299.

DOI：10.1080/00949650802627402

• average run length (ARL)
• inverse Gaussian (Wald) distribution
• control charts
• false alarm rate
• parametric bootstrap
• percentile
• Shewhart chart

The conventional Shewhart-type control chart is developed essentially on the central limit theorem. Thus, the Shewhart-type control chart performs particularly well when the observed process data come from a near-normal distribution. On the other hand, when the underlying distribution is unknown or non-normal, the sampling distribution of a parameter estimator may not be available theoretically. In this case, the Shewhart-type charts are not available. Thus, in this paper, we propose a parametric bootstrap control chart for monitoring percentiles when process measurements have an inverse Gaussian distribution. Through extensive Monte Carlo simulations, we investigate the behaviour and performance of the proposed bootstrap percentile charts. The average run lengths of the proposed percentage charts are investigated.

• 出版日期2010