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Journal ArticleDOI

Sample Size for Estimating Multinomial Proportions

Steven K. Thompson
- 01 Feb 1987 - 
- Vol. 41, Iss: 1, pp 42-46
TLDR
In this article, a procedure and a table for selecting sample size for simultaneously estimating the parameters of a multinomial distribution is presented, analogous to the case in which a binomial parameter equals one-half.
Abstract
This article presents a procedure and a table for selecting sample size for simultaneously estimating the parameters of a multinomial distribution. The results are obtained by examining the “worst” possible value of a multinomial parameter vector, analogous to the case in which a binomial parameter equals one-half.

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Citations
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Journal ArticleDOI

Study Designs and Tests for Comparing Resource Use and Availability II

TL;DR: It is illustrated that resource selection models are part of a broader collection of statistical models called weighted distributions and recommend some promising areas for future development.
Journal ArticleDOI

Sample size determination : a review

TL;DR: In this paper, a small number of simple problems, such as estimating the mean of a normal distribution or the slope in a regression equation, are covered, and some key techniques are presented.
Journal ArticleDOI

Simultaneous Confidence Intervals and Sample Size Determination for Multinomial Proportions

TL;DR: In this paper, two new simultaneous confidence interval procedures for multinomial proportions are introduced and compared with the established ones, where the accuracy of the procedure is measured by the volume of the confidence region corresponding to the nominal coverage probability and the probability of coverage it achieves.

An introduction to the application of (case 1) best–worst scaling in marketing research

TL;DR: It is demonstrated how to use BWS to measure subjective quantities in two different empirical examples, which measures preferences for weekend getaways and requires comparing relatively few objects.
Journal ArticleDOI

An introduction to the application of (case 1) best–worst scaling in marketing research

TL;DR: In this article, the authors review and discuss recent developments in best-worst scaling (BWS) that allow researchers to measure items or objects on measurement scales with known properties, and demonstrate how to use BWS to measure subjective quantities in two different empirical examples.
References
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Book

Simultaneous Statistical Inference

TL;DR: In this article, the authors presented a case of two means regression method for the family error rate, which was used to estimate the probability of a family having a nonzero family error.
Journal ArticleDOI

On Simultaneous Confidence Intervals for Multinomial Proportions

TL;DR: In this article, the authors presented a method for obtaining simultaneous confidence intervals for the parameters of a multinomial distribution, and compared this method with the one suggested recently by Quesenberry and Hurst (1964).
Journal ArticleDOI

Handbook of the Normal Distribution.

TL;DR: Genesis: an historical background basic properties expansions and algorithms characterizations sampling distributions limit theorems and expansions normal approximations to distributions order statistics from normal samples the bivariate normal distribution Bivariate normal sampling distributions point estimation statistical intervals as discussed by the authors.
Journal ArticleDOI

Large Sample Simultaneous Confidence Intervals for Multinomial Proportions

TL;DR: In this paper, a method for obtaining a set of simultaneous confidence intervals for the probabilities of a multinomial distribution is presented, where a large sample size is assumed and a lower bound given for the confidence coefficient.
Journal ArticleDOI

A Note on Sample Size Estimation for Multinomial Populations

TL;DR: In this article, a method for determining the sample size required for a specified precision simultaneous confidence statement about the parameters of a multinomial population is described, based on a simultaneous confidence interval procedure due to Goodman.