Mathematical aspects of estimating two treatment effects and a common variance in an assured allocation design

Bruce Levin, Herbert Robbins, Cunhui Zhang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We consider a doubly semi-parametric model for normally distributed random variables which arises in experiments with an assured allocation design. In settling a curious question about estimation of the model's variance parameter, a certain inequality arises that involves the normal probability density function and its first two integrals. The inequality is of mathematical interest in its own right, and is given a rigorous proof.

Original languageEnglish (US)
Pages (from-to)255-262
Number of pages8
JournalJournal of Statistical Planning and Inference
Volume108
Issue number1-2
DOIs
StatePublished - Nov 1 2002

Fingerprint

Treatment Effects
Semiparametric Model
Random variables
Probability density function
Random variable
Experiment
Experiments
Design
Treatment effects
Model
Integral
Semiparametric model

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Mathematical aspects of estimating two treatment effects and a common variance in an assured allocation design. / Levin, Bruce; Robbins, Herbert; Zhang, Cunhui.

In: Journal of Statistical Planning and Inference, Vol. 108, No. 1-2, 01.11.2002, p. 255-262.

Research output: Contribution to journalArticle

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