Decision Sciences Journal
Volume 30, Number 3
Summer 1999
Effectiveness of Joint Estimation When the Outlier Is the
Last Observation in an Autocorrelated Short Time Series
Christine M. Wright
Department of Management, College of Business, Western Carolina
University,
Cullowhee, NC 28723, email: cwright@wpoff.wcu.edu
Michael Y. Hu
Department of Marketing, College of Business Administration,
Kent State University, Kent, OH 44242
David E. Booth
Department of Management, College of Business Administration,
Kent State University, Kent, OH 44242
ABSTRACT. The effectiveness of the joint estimation
(JE) outlier detection method as a process control technique
for short autocorrelated time series is investigated and compared
with exponentially weighted moving average (EWMA). The research
goal is to determine the effectiveness of the method for detecting
out-of-control observations when they are the last observation
in a short autocorrelated time series. This is an important problem
because detecting an outlier in the period when it occurs, rather
than several periods after it occurs, will preclude the production
of more defective units. Two cases are investigated: short simulated
time series when normality is assumed, and short real time series
when the assumption is violated. The results show that JE is
effective for short time series, particularly for autoregressive
series when normality is violated. Joint estimation is also effective
for moving average series under the normality assumption and
less effective when the assumption is violated. In all cases,
JE is found to be more effective than EWMA.
Subject Areas: Quality, SPC, and Time Series. |