The Annals of Statistics

Rank Score Comparison of Several Regression Parameters

J. N. Adichie

Full-text: Open access

Abstract

For testing $\beta_i = \beta, i = 1,\cdots, k$, in the model $Y_{ij} = \alpha + \beta_iX_{ij} + Z_{ij} j = 1,\cdots, n_i$ a class of rank score tests is presented. The test statistic is based on the simultaneous ranking of all the observations in the different $k$ samples. Its asymptotic distribution is proved to be chi square under the hypothesis and noncentral chi square under an appropriate sequence of alternatives. The asymptotic efficiency of the given procedure relative to the least squares procedure is shown to be the same as the efficiency of rank score tests relative to the $t$-test in the two sample problem. The proposed criterion would be an asymptotically most powerful rank score test for the hypothesis if the distribution function of the observations is known.

Article information

Source
Ann. Statist., Volume 2, Number 2 (1974), 396-402.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176342676

Digital Object Identifier
doi:10.1214/aos/1176342676

Mathematical Reviews number (MathSciNet)
MR423669

Zentralblatt MATH identifier
0277.62051

JSTOR
links.jstor.org

Subjects
Primary: 62G10: Hypothesis testing
Secondary: 62G20: Asymptotic properties 62E20: Asymptotic distribution theory 62J05: Linear regression

Keywords
Simultaneous ranking score generating function bounded in probability orthogonal transformation asymptotic normality asymptotic efficiency

Citation

Adichie, J. N. Rank Score Comparison of Several Regression Parameters. Ann. Statist. 2 (1974), no. 2, 396--402. doi:10.1214/aos/1176342676. https://projecteuclid.org/euclid.aos/1176342676


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