Some Recent Research Progress in Statistical Process Control

Organizer and Session Chair: Peihua Qiu, University of Minnesota


Control Charts for Health Care Monitoring Under Overdispersion

Willem Albers
Department of Applied Mathematics, University of Twente

An attractive way to control attribute data from high quality processes is to wait till r>=1 failures have occurred. The choice of r in such negative binomial charts is dictated by how much the failure rate is supposed to change during Out-of-Control. However, these results have been derived for the case of homogeneous data. Especially in health care monitoring, (groups of) patients will often show large heterogeneity. In the present paper we will show how such overdispersion can be taken into account. In practice, typically neither the average failure rate, nor the overdispersion parameter(s), will be known. Hence we shall also derive and analyze the estimated version of the new chart.


Statistical Methods for Profiling Nanopartciles

Yu Ding
Industrial and Systems Engineering, Texas A&M University

Nanoparticles have many potential applications because of their unique and interesting properties shown in the nanoscale. These properties are highly correlated with the size and shape of nanoparticles. For this reason, measuring and profiling nanoparticles is a crucial step in controlling the synthesis process of nanpoparticles. This talk presents our recent efforts in applying various statistical methods for the purpose of profiling nanoparticles.


Statistical Monitoring of Univariate Non-Gaussian Processes

Peihua Qiu
School of Statistics, University of Minnesota

In practice, observed process measurements are often non-Gaussian. Statistical monitoring of non-Gaussian processes is thus an important problem. This talk focuses on appropriate monitoring of univariate non-Gaussian processes. In the literature, there have been some existing methods for handling this problem, under the names of distribution-free or nonparametric SPC control charts. Most of them are based on ranking information of the process measurement data observed at different time points. In our recent research, we propose several alternative control charts that are appropriate for monitoring univariate non-Gaussian processes. We also make a systematic comparison among various nonparametric control charts, including the ones newly proposed. Certain practical guidelines are provided for practitioners to properly use and choose among these control charts. This is joint research with Mr. Zhonghua Li.