Measuring the Significance of Correlated Diagnostics Statistics
by Jeremy Penzer.
Abstract:
Problems involving calibration of sequences of correlated statistics arises in time series diagnostics. Critical values are required to locate unusual points from sequences of diagnostic statistics. We put forward simple criteria, which reliably identify significant outliers, level shifts and changes in seasonal pattern. The circumstances under which the correlation of sequences of statistics becomes important in determining critical values are discussed. These tests are applied to real data sets to detect behaviour previously overlooked.
Key Words:
cBonferonni inequalities, Change point detection, Order statistics, Outliers, Level shifts, Structural breaks, Structural time series models
Author:
Jeremy Penzer, j.penzer@lse.ac.uk
Editor:
R. G. Graf,rgraf@sunstroke.sdsu.edu
READING THE ARTICLE: You can read the article in
portable document (.pdf) format (196637 bytes.)
NOTE: The content of this article is the intellectual property of the authors, who retains all rights to future publication.
This page has been accessed 349 times since July 24, 2006.
Return to the 
Home Page.