Inference for the model of several treatments and a control

Arthur Cohen; H. B. Sackrowitz

doi:https://doi.org/10.1016/S0378-3758(02)00245-8

Inference for the model of several treatments and a control

Arthur Cohen, H. B. Sackrowitz

School of Arts and Sciences, Statistics

Rutgers, The State University

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Cohen et al. (2000, J. Multivariate Anal., 72, 50-77) noted that for the tree-order model, under exponential family distribution theory, likelihood inference may have shortcomings. In light of this we offer alternative inference procedures for this model under normal distribution theory and in the nonparametric case as well. We suggest new estimators that are not only free of the practical shortcomings of maximum likelihood estimators, but that have other desirable properties as well. We suggest an alternative to the likelihood ratio test which has desirable practical properties and competitive power properties. In the nonparametric case we offer an alternative to the popular tests based on ranks.

Original language	American English
Pages (from-to)	89-101
Number of pages	13
Journal	Journal of Statistical Planning and Inference
Volume	107
Issue number	1-2
DOIs	https://doi.org/10.1016/S0378-3758(02)00245-8
State	Published - Sep 1 2002

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

Keywords

Cone-order monotonicity
Dunnett's test
Likelihood inference
Preservation of estimators
Reversals of estimators
Tree-order model

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https://doi.org/10.1016/S0378-3758(02)00245-8

Cite this

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abstract = "Cohen et al. (2000, J. Multivariate Anal., 72, 50-77) noted that for the tree-order model, under exponential family distribution theory, likelihood inference may have shortcomings. In light of this we offer alternative inference procedures for this model under normal distribution theory and in the nonparametric case as well. We suggest new estimators that are not only free of the practical shortcomings of maximum likelihood estimators, but that have other desirable properties as well. We suggest an alternative to the likelihood ratio test which has desirable practical properties and competitive power properties. In the nonparametric case we offer an alternative to the popular tests based on ranks.",

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AU - Sackrowitz, H. B.

N1 - Funding Information: Research supported by the National Science Foundation, NSF Grant 9618715, and NSA MDA 904-98-1-0506.

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AB - Cohen et al. (2000, J. Multivariate Anal., 72, 50-77) noted that for the tree-order model, under exponential family distribution theory, likelihood inference may have shortcomings. In light of this we offer alternative inference procedures for this model under normal distribution theory and in the nonparametric case as well. We suggest new estimators that are not only free of the practical shortcomings of maximum likelihood estimators, but that have other desirable properties as well. We suggest an alternative to the likelihood ratio test which has desirable practical properties and competitive power properties. In the nonparametric case we offer an alternative to the popular tests based on ranks.

KW - Cone-order monotonicity

KW - Dunnett's test

KW - Likelihood inference

KW - Preservation of estimators

KW - Reversals of estimators

KW - Tree-order model

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Inference for the model of several treatments and a control

Abstract

ASJC Scopus subject areas

Keywords

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