Exponentially weighed moving average charts for monitoring zero-inflated proportions with applications in health care.

In the context of public health surveillance, the aim is to monitor the occurrence of health-related events. Among them, statistical process monitoring focuses very often on the monitoring of rates and proportions (i.e.

Efficient algorithms for calculating the probability distribution of the sum of hypergeometric-distributed random variables.

In probability theory and statistics, the probability distribution of the sum of two or more independent and identically distributed (i.i.d.

The EWMA sign chart revisited: performance and alternatives without and with ties.

The EWMA Sign control chart is an efficient tool for monitoring shifts in a process regardless the observations' underlying distribution. Recent studies have shown that, for nonparametric control charts, due to the discrete nature of the statistics being used (such as the Sign statistic), it is impossible to accurately compute their Run Length properties using Markov chain or integral equation methods. In this work, a modified nonparametric Phase II EWMA chart based on the Sign statistic is proposed and its Run Length properties are discussed.

A one-sided exponentially weighted moving average control chart for time between events.

Exponentially weighted moving average (EWMA) control charts for time-between-events (TBE) are commonly suggested to monitor high-quality processes for the early detection of process deteriorations. In this study, an enhanced one-sided EWMA TBE scheme is developed for rapid detection of increases or decreases in the process mean. The use of the truncation method helps to improve the sensitivity of the proposed scheme in the process mean detection.

A combined mixed--skip sampling strategy to reduce the effect of autocorrelation on the X̄ scheme with and without measurement errors.

In order to reduce the effect of autocorrelation on the monitoring scheme, a new sampling strategy is proposed to form rational subgroup samples of size . It requires sampling to be done such that: (i) observations from two consecutive samples are merged, and (ii) some consecutive observations are skipped before sampling. This technique which is a generalized version of the mixed samples strategy is shown to yield a better reduction of the negative effect of autocorrelation when monitoring the mean of processes with and without measurement errors.

A distribution-free EWMA control chart for monitoring time-between-events-and-amplitude data.

Many control charts have been developed for the simultaneous monitoring of the time interval between successive occurrences of an event E and its magnitude . All these TBEA (Time Between Events and Amplitude) control charts assume a known distribution for the random variables and . But, in practice, as it is rather difficult to know their actual distributions, proposing a distribution free approach could be a way to overcome this 'distribution choice' dilemma.

Cumulative sum control charts for monitoring geometrically inflated Poisson processes: An application to infectious disease counts data.

In this work, we study upper-sided cumulative sum control charts that are suitable for monitoring geometrically inflated Poisson processes. We assume that a process is properly described by a two-parameter extension of the zero-inflated Poisson distribution, which can be used for modeling count data with an excessive number of zero and non-zero values. Two different upper-sided cumulative sum-type schemes are considered, both suitable for the detection of increasing shifts in the average of the process.