Use of Linear Logistic Model for the Analysis of Sensory Evaluation Data

Newell, G. J.

doi:10.1111/j.1365-2621.1982.tb12722.x

Cited by 9 publications

(1 citation statement)

References 17 publications

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“…The first is on an anti-smoking advertisement (Gelb and Pickett, 1983), the second is a medical study on rheumatoid arthritis (Koch, 1985, pg. 163), and the third is on a yogurt sensory evaluation (Newell, 1982). We use these data sets to demonstrate inference for multinomial populations under the SOR when the SOR holds with certainty and with uncertainty.…”

Section: Empirical Studiesmentioning

confidence: 99%

Bayesian inference for multinomial populations under stochastic ordering

Bhattacharya

Nandram

1996

Journal of Statistical Computation and Simulation

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In many parametric problems the use of order restrictiocs among the parameters can lead to improved precision. Our interest is in the study of several multinomial populations under the stochastic order restriction (SOR) for univariate situations. We use Bayesian methods to show that the SOR can lead to larger gains in precision than the method without the SOR when the SOR is reasonable. Unlike frequentist order restricted inference, our methodology permits analysis even when there is uncertainty about the SOR. Our method is sampling based, and we use simple and efficient rejection sampling. The Bayes factor in favor of the SOR is computed in a simple manner, and samples from the requisite posterior distributions are easily obtained. We use real data to illustrate the procedure, and we show that there is likely to be larger gains in precision under the SOR.

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Section: Empirical Studiesmentioning

confidence: 99%