Abstract
This paper aims to demonstrate the use of pre whitening (PW) technique to handle the presence of autocorrelation on the statistical control charts, for 3-year (2008-2010) daily pediatrics (less than 4 years old) hospital admission.The PW technique has been implemented as an alternative procedure to obtain residuals series which are statistically uncorrelated to each other.Results showed that there is a reduction in the number of out-of–control signals in residual series control chart as compared to the amount of the out-of–control signals on traditional statistical control chart before the use of PW technique.Thus, it is suggested that statistical control chart using residual series performs better when the original pediatric hospital admission series are auto-correlated. In addition, it can be concluded that the Phase II (monitoring period) performance process is likely to follow the similar pattern of the Phase I (baseline period) process except for only one day on the 13th October 2009 that exceeds the upper control limit.This means that the pediatrics hospital admission on that particular day has not improved and fundamentally changed from what are expected in stable process.
Highlights
Standard statistical control charts are based on the assumption that the observations obtained at each time period are independent, which implies that there is no relationship between adjacent observations
The autoregressive integrated moving average (ARIMA) model is one of the most popular methods to deal with autocorrelation in time series model
The aim of this paper is to use PW technique to handle the presence of autocorrelation on the statistical control charts of 3-year (2008-2010) daily hospital admission of children aged less than 4 years old
Summary
Standard statistical control charts are based on the assumption that the observations obtained at each time period are independent, which implies that there is no relationship between adjacent observations. The presence of autocorrelation results in a number of problems such as an increase in type I error rate and thereby increases the number of false alarm [2]. For these reasons, handling and testing the auto-correlated process is an important procedure before constructing any statistical control chart. There exists many different techniques to handle the effect of autocorrelation in process observations [3, 4]. Various authors have suggested fitting an appropriate time series model to the observations and applying traditional control charts to the process residuals [5]. Some of the model identification and model parameter estimation in ARIMA model are difficult to determined and time series modeling knowledge is needed for constructing the ARIMA model [6, 7]
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