Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/10638
Title: | MONITORING AUTOCORRELATED PROCESSES |
Authors: | Tang, Weiping |
Advisor: | Viveros-Aguilera, Roman Angelo Canty, Shui Feng Angelo Canty, Shui Feng |
Department: | Mathematics and Statistics |
Keywords: | Autoregressive integrated moving average;Dynamic mean shift;Forecast recovery;Monte Carlo simulation;non-homogeneous Markov chain;One-sided EWMA;Statistical Methodology;Statistical Models;Statistical Theory;Statistical Methodology |
Publication Date: | Oct-2011 |
Abstract: | <p>Several control schemes for monitoring process mean shifts, including cumulative sum (CUSUM), weighted cumulative sum (WCUSUM), adaptive cumulative sum (ACUSUM) and exponentially weighted moving average (EWMA) control schemes, display high performance in detecting constant process mean shifts. However, a variety of dynamic mean shifts frequently occur and few control schemes can efficiently work in these situations due to the limited window for catching shifts, particularly when the mean decreases rapidly. This is precisely the case when one uses the residuals from autocorrelated data to monitor the process mean, a feature often referred to as forecast recovery. This thesis focuses on detecting a shift in the mean of a time series when a forecast recovery dynamic pattern in the mean of the residuals is observed. Specifically, we examine in detail several particular cases of the Autoregressive Integrated Moving Average (ARIMA) time series models. We introduce a new upper-sided control chart based on the Exponentially Weighted Moving Average (EWMA) scheme combined with the Fast Initial Response (FIR) feature. To assess chart performance we use the well-established Average</p> <p>Run Length (ARL) criterion. A non-homogeneous Markov chain method is developed for ARL calculation for the proposed chart. We show numerically that the proposed procedure performs as well or better than the Weighted Cumulative Sum (WCUSUM) chart introduced by Shu, Jiang and Tsui (2008), and better than the conventional CUSUM, the ACUSUM and the Generalized Likelihood Ratio Test (GLRT) charts. The methods are illustrated on molecular weight data from a polymer manufacturing process.</p> |
Description: | <p>This thesis is submitted by Weiping Tang on August 2, 2011.</p> |
URI: | http://hdl.handle.net/11375/10638 |
Identifier: | opendissertations/5670 6690 2128865 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Size | Format | |
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fulltext.pdf | 1.34 MB | Adobe PDF | View/Open |
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