A preliminary Cusum chart based on individual observations is developed from the uniformly most powerful test for the detection of linear trends. This Cusum chart is compared with several of its competitors which are based on the likelihood ratio test and on transformations of standardized...

Using Markov chain representations, we evaluate and compare the performance of cumulative sum (CUSUM) and Shiryayev-Roberts methods in terms of the zero- and steady-state average run length and worst-case signal resistance measures. We also calculate the signal resistance values from the worst-...

In this paper a general method of constructing control charts for preliminary analysis of individual observations is presented, which is based on recursive score residuals. A simulation study shows that certain implementations of these charts are highly effective in detecting assignable causes.

Maintaining a stable score scale over time is critical for all standardized educational assessments. Traditional quality control tools and approaches for assessing scale drift either require special equating designs, or may be too time-consuming to be considered on a regular basis with an...

This article presents a single CUSUM scheme (called the GCUSUM chart) for simultaneously monitoring the time interval T and magnitude X of an event. For example, a traffic accident may be considered as an event, and the total loss in dollars in each case is the event magnitude. Since the GCUSUM...

Purpose – The purpose of this paper is to study the effect of correlation on the performance of CUSUM and EWMA charts. The performance of the CUSUM and EWMA charts is measured in terms of average run lengths (ARLs) for the positively correlated data. The ARLs at various set of parameters of...

In this paper a general method of constructing control charts for preliminary analysis of individual observations is presented, which is based on recursive score residuals. A simulation study shows that certain implementations of these charts are highly effective in detecting assignable causes.