No abstract

No abstract

No abstract

No abstract

A standard cumulative sum (CUSUM) chart for controlling the process mean takes samples from the process at fixed-length sampling intervals and uses a control statistic based on a cumulative sum of differences between the sample means and the target value. This article proposes a modification of the standard CUSUM scheme that varies the time intervals between samples depending on the value of the CUSUM control statistic. The variable sampling interval (VSI) CUSUM chart uses short sampling intervals if there is an indication that the process mean may have shifted and long sampling intervals if there is no indication of a change in the mean. If the CUSUM statistic actually enters the signal region, then the VSI CUSUM chart signals in the same manner as the standard CUSUM chart. A Markov-chain approach is used to evaluate properties such as the average time to signal and the average number of samples to signal. Results show that the proposed VSI CUSUM chart is considerably more efficient than the standard CUSUM chart. Tables are provided to aid in the design of VSI CUSUM charts.

This study uses disease surveillance cluster analysis methods to identify clusters for the three most common pediatric cancers in Florida (brain tumors, leukemia, lymphoma). In addition to a univariate purely spatial analysis, a space-time analysis is done, followed by a nonparametric permutation test for space-time interaction. This is followed by a multivariate spatial analysis for the three pediatric cancer types together. The main findings include a significant spatial cluster for each of the three cancer types covered in this study, in addition to identifying two significant clusters for all pediatric cancer types combined.

No abstract

No abstract