Multivariate linear rank statistics for profile analysis

Vernon Chinchilli, Pranab Kumar Sen

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

For some general multivariate linear models, linear rank statistics are used in conjunction with Roy's Union-Intersection Principle to develop some tests for inference on the parameter (vector) when they are subject to certain linear constraints. More powerful tests are designed by incorporating the a priori information on these constraints. Profile analysis is an important application of this type of hypothesis testing problem; it consists of a set of hypothesis testing problem for the p responses q-sample model, where it is a priori assumed that the response-sample interactions are null.

Original languageEnglish (US)
Pages (from-to)219-229
Number of pages11
JournalJournal of Multivariate Analysis
Volume12
Issue number2
DOIs
StatePublished - Jan 1 1982

Fingerprint

Linear Rank Statistics
Hypothesis Testing
Union-intersection Principle
Statistics
Multivariate Linear Model
Testing
Linear Constraints
Null
Interaction
Profile
Hypothesis testing
Model
Inference

All Science Journal Classification (ASJC) codes

  • Statistics, Probability and Uncertainty
  • Numerical Analysis
  • Statistics and Probability

Cite this

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Multivariate linear rank statistics for profile analysis. / Chinchilli, Vernon; Sen, Pranab Kumar.

In: Journal of Multivariate Analysis, Vol. 12, No. 2, 01.01.1982, p. 219-229.

Research output: Contribution to journalArticle

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