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Various methods based on Student t variates have been suggested and used for obtaining simultaneous confidence intervals for several means, or for several contrasts among means. Determination of an overall confidence level for such intervals involves evaluating the probability mass of a multivariate t distribution over a hypercube centered at the origin, with sides paralleling the coordinate planes, or obtaining bounds for this probability mass. Since such distributions involve many nuisance parameters, an impossible number of tables would be necessary in order to make exact confidence intervals. In the virtual absence of tables, approximations and bounds become important. In this paper, an attempt has been made to investigate the adequacy of certain suggested approximations [2], [5], [8] by computing the exact distributions for some particular cases. These exact distributions have been compared with approximations. This paper is concerned with two-sided confidence intervals, rather than one-sided intervals. [ABSTRACT FROM AUTHOR]

The decision-theoretic approach to point estimation involves the choice of an estimate to minimize the expected loss associated with the estimate. The purpose of this paper is to indicate the influence of risk aversion on point estimates for classes of payoff functions including the piecewise linear and quadratic payoff functions. Increased risk aversion results in a point estimate closer to zero for a quadratic pay. off function and a lower estimate with a piecewise linear payoff function, for example. [ABSTRACT FROM AUTHOR]

The article discusses assessing prior distributions for applying Bayesian statistics in auditing. The results of this study suggest that auditors are willing to specify information from which prior distributions can be constructed. The prior distributions which were obtained, had most of the probability concentrated on small amounts of error, but there was considerable variability among them. It was found that there were substantial inconsistencies in the way some auditors specified information about the prior distributions. These inconsistencies and the variability among the prior distributions indicate that at least some of these distributions do not accurately reflect the auditors' beliefs about audit populations. Therefore, it is concluded that auditors should proceed with caution in relying on their prior distributions. To apply Bayesian techniques, the auditor subjectively evaluates the non-sampling evidence and expresses his belief about the audit population as a prior probability distribution. A likelihood function is then obtained by statistically evaluating the sample result.