Control Chart for Autocorrelated Processes with Heavy Tailed Distributions
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Keoagile Thaga
Abstract
Standard control charts are constructed under the assumption that the observations taken from the process of interest are independent over time; however, in practice the observations in many cases are actually correlated. This paper considers the problem of monitoring a process in which the observations can be represented as a first-order autoregressive model following a heavy tailed distribution. We propose a chart based on computing the control limits using the process mean and the standard error of the least absolute deviation for the case when the process quality characteristics follows a heavy tailed t-distribution. This chart has narrow control limits since the standard error of the least absolute deviation is smaller than that of the ordinary least square estimator in the case of heavy tailed distributions.
© Heldermann Verlag
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Artikel in diesem Heft
- Stochastic Measurement Procedures Based on Stationary Time Series
- Semi-Parametric Estimation of PX,Y (X > Y)
- On The Performance of A New Test of Exponentiality Against IFR Alternatives Based on the L-statistic Approach
- Control Chart for Autocorrelated Processes with Heavy Tailed Distributions
- Designing the Scale Counting Procedure for Large Numbers of Small Parts
- MTBF for K-out-of-N: G Systems with M Failure Modes
- Non-parametric Control Chart for Controlling Variability Based on Rank Test
- Bounds for Distorted Risk Measures
- Parameter Estimation for the Bivariate Exponential Distribution by the EM Algorithm Based on Censored Samples
- Explicit Expressions for Moments of Log Normal Order Statistics
- Partially Specified Prior
- The Need for a Standard for Making Predictions
