Phase I Analysis of Variables Type Correlated Data

Abstract

Phase I analysis of a control chart implementation comprises parameter estimation, chart design, and outlier filtering, which are performed iteratively until reliable control limits are obtained. These control limits are then used in Phase II for online monitoring and prospective analyses of the process to detect out-of-control states.Although a Phase I study is required only when the true values of the parameters of a process are unknown, this is the case in many practical applications. It is also well known that autocorrelation effects parameter estimation and so control chart design. In this thesis, AutoRegressive models of order 1, AR(1), are considered and the effects of two extreme cases for Phase I analysis are studied; the case where all outliers are filtered from the data set. Performance of the maximum likelihood and conditional sum of squares estimators are evaluated.