Extended critical vawes of the multivariate extreme deviate test for detecting a single spurious observation
Communications in Statistics - Simulation and ComputationPublished 1 January 1988
Linda W. Jennings, Dean M. Young
Citations17
SJR quartileQ2
SJR score0.43
SNIP1.00
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
The Institute of Mathematical Statistics has published a table of critical values for the multivariate extreme deviate test. However, the critical values, derived by a Monte Carlo simulation, are given for only the dimensions 2 through 5. We present new critical values for the dimensions 6 through 10, 12, 15, and 20. The results are presented in both table and graphical form. All critical values for the test statistic have been generated by a Monte Carlo simulation using 10,000 observations per case. An example is presented using the new critical values.
Keywords
MathematicsDecision Sciences
Identification of Outliers
2,808 Citations1980D. M. Hawkins
A computer normalizes the one or more sets of historical data points and creates a first visual representation corresponding to the first set of the oneor more sets and the second set of additional points.
Journal of the American Statistical AssociationOn Tests for Multivariate Normality
287 Citations1973J. F. Malkovich, A. A. Afifi
Annals of EugenicsA BIOMETRIC INVESTIGATION OF TWINS AND THEIR BROTHERS AND SISTERS
48 Citations1933Percy Stocks
Handbook of statistics24 Computations of some multivariate distributions
27 Citations1980P. R. Krishnaiah
The chapter discusses certain computational aspects of several multivariate distributions and provides percentage points of some of these distributions, which are useful in the application of the finite intersection tests for multiple comparisons of the means and mean vectors of univariate and multivariate normal populations.
