The effect of autocorrelation on the ewma maxmin tolerance limits

Amin, Raid; Li, Kuiyuan

doi:10.1080/00949650214263

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Intuitively, this is because the process approaches the independent setting, which is the setting that the k ‐factor in Equation was derived under. This behavior is comparable to the simulation results reported in Amin and Li and Knoth and Amin . However, here we have reported results from a more expansive set of simulation conditions such that the resulting figure gives a clear indication of the shape of this behavior.…”

Section: Adjusted Confidence Level and Coverage Studysupporting

confidence: 90%

“…For the AR(1) model, Amin and Li proposed using the usual sample variance or the estimate of the true process variance. Let us consider a time series of length T .…”

Section: Tolerance Intervals For Autoregressive Modelmentioning

confidence: 99%

“…One setting for which tolerance intervals have minimal development is time series. The effects of autocorrelation on normal‐based tolerance intervals is treated in Amin and Lee, and was further studied by Amin and Li to understand those same effects on exponentially weighted moving average tolerance limits . use the process variance from an autoregressive model of order 1 (AR(1)) in the calculation of S in Equation .…”

Section: Introductionmentioning

confidence: 99%

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Tolerance intervals for autoregressive models, with an application to hospital waiting lists

Cheng

Young

2020

Appl Stoch Models Bus & Ind

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Long waiting lists are a symbol of inefficiencies of hospital services. The dynamics of waiting lists are complex, especially when trying to understand how the lists grow due to the demand of a particular treatment relative to a hospital's capacity. Understanding the uncertainty of forecasting growth/decline of waiting lists could help hospital managers with capacity planning. We address this uncertainty through the use of statistical tolerance intervals, which are intervals that contain a specified proportion of the sampled population at a given confidence level. Tolerance intervals are available for numerous settings, however, the approaches for autoregressive models are far more limited. This article fills that gap and establishes tolerance intervals for general AR(p) models, which may also have a mean or trend component present. A rigorous development of tolerance intervals in this setting is presented. Extensive simulation studies identify that good coverage properties are achieved when the AR process is stationary and the parameters of the AR model are well within the stationarity constraints. Otherwise, a bootstrap-based correction can be applied to improve the coverage probabilities. Finally, the method is applied to the monthly number of patients on hospital waiting lists in England. K E Y W O R D Sk-factor, bootstrap, capacity planning, coverage probability, forecasting, regression 1 268

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Section: Adjusted Confidence Level and Coverage Studysupporting

confidence: 90%

“…For the AR(1) model, Amin and Li proposed using the usual sample variance or the estimate of the true process variance. Let us consider a time series of length T .…”

Section: Tolerance Intervals For Autoregressive Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tolerance intervals for autoregressive models, with an application to hospital waiting lists

Cheng

Young

2020

Appl Stoch Models Bus & Ind

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“…See, for example, Box et al (1974), Vasilopoulos and Stamboulis (1978), Alwan and Roberts (1988), and Wardell et al (1992) to name just a few. Studies on control charts with autocorrelated data can also be found by, e.g., Wardell et al (1994), Faltin et al (1997), Lu and Reynolds (1999), Amin and Li (2002), and Knoth and Amin (2003). Woodall and Montgomery (1999), Stoumbos et al (2000) and Woodall (2000) provided discussions and future research issues in statistical process control.…”

Section: Introductionmentioning

confidence: 98%

Simulated annealing procedure for on-line control with process dynamics

Chou

Wang

2007

International Journal of Systems Science

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We study the problem of economic design for a control policy where the sampling interval ( m) and control limit (d) are determined by minimizing the expected total quality costs. In this article, we propose a simulated annealing scheme applicable to an economic design for a control policy where the disturbances can follow the autoregressive integrated moving average (ARIMA) processes and obtain the optimum d and m. Numerical investigations are conducted for a system with a first-order dynamics subjected to an IMA ( 1,1), and an ARI ( 1,1) random disturbance. The performance of the proposed simulated annealing scheme is very promising and compares favorably to other methods

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Effect of autocorrelation on the performance of EWMA chart

Singh

Prajapati

2012

IJPQM

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The effect of autocorrelation on the ewma maxmin tolerance limits

Cited by 4 publications

References 22 publications

Tolerance intervals for autoregressive models, with an application to hospital waiting lists

Tolerance intervals for autoregressive models, with an application to hospital waiting lists

Simulated annealing procedure for on-line control with process dynamics

Effect of autocorrelation on the performance of EWMA chart

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