Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the C...

In certain statistical process control applications, quality of a process or product can be characterized by a function commonly referred to as profile. Some of the potential applications of profile monitoring are cases where quality characteristic of interest is modelled using binary,multinomial or ordinal variables. In this paper, profiles with multinomial response are studied. For this purpo...

When data consist of patients with a specific risk to develop an health event and we are interested in monitoring the incidence rate of this event, the standard Bernoulli CUSUM fails and a risk-adjustment needs. The risk-adjusted CUSUM chart proposed by Steiner et al. (2000) shows some limitations. To overcome these limits a simple risk-adjusted CUSUM is proposed and it may be considered a gene...

A theoretical analysis of the cumulative sum (CUSUM) technique for detecting a series of time signals from noisy background is provided. The statistic using CUSUM-slope is introduced as a measure for capturing the average of signals within the time-window, in which the slope is computed. This provides a time-independent method for estimating the signal content within the time-window. The detect...

We introduce a novel dynamic model for discrete time-series data, in which the temporal sampling may be nonuniform. The model is specified by constructing a hierarchy of Poisson factor analysis blocks, one for the transitions between latent states and the other for the emissions between latent states and observations. Latent variables are binary and linked to Poisson factor analysis via Bernoul...

Control charts with exponentially weighted moving average (EWMA) statistics (mean and variance) are used to jointly monitor the mean and variance of a process. An EWMA cost minimization model is presented to design the joint control scheme based on pure economic or both economic and statistical performance criteria. The pure economic model is extended to the economic-statistical design by addin...

Not all data in practice came from a process with normal distribution. When the process distribution is non-normal or unknown, the commonly used Shewhart control charts are not suitable. In this paper, a new non-parametric CUSUM Mean Chart is proposed to monitor the possible small mean shifts in the process. The sampling properties of the new monitoring statistics are examined and the average r...

We develop an empirical approximation to the null steady-state distribution of the cumulative sum (CUSUM) statistic, defined as the distribution of values obtained by running a CUSUM with no upper boundary under the null state for an indefinite period of time. The derivation is part theoretical and part empirical and the approximation is valid for CUSUMs applied to Normal data with known varian...