Effect of Prior Specification on Bayesian Design for Two-Sample Comparison of a Binary Outcome

Closed forms of the optimal Bayesian design for the estimation of the log odds-ratio from a two-sample experiment are obtained. The effect of various specifications of prior distributions are considered and plausible circumstances where unequal allocation is optimal are presented.     

Spreadsheets in Statistical Practice—Another Look

Many authors have criticized the use of spreadsheets for statistical data processing and computing because of incorrect statistical functions, no log file or audit trail, inconsistent behavior of computational dialogs, and poor handling of missing values. Some improvements in some spreadsheet processors and the possibility of audit trail facilities suggest that the use of a spreadsheet for some statistical data entry and simple analysis tasks may now be acceptable. A brief outline of some issues and some guidelines for good practice are included.     

Rank-Based Classification Using Robust Discriminant Functions

The Pareto distribution model assumption in the peaks over threshold method, will be tested by making using of the Kolmogorov–Smirnov goodness of fit method. Pareto distributed variables can be transformed to exponential, and the test will be for exponentiality. It was found that the statistic can be used as an indication of where to choose the threshold and to check the Pareto model assumption.     

Application of the Generalized Likelihood Ratio Test for Detecting Changes in the Mean of Multivariate GARCH Processes

We derive several multivariate control charts to monitor the mean vector of multi-variate GARCH processes under the presence of changes, by means of maximizing the generalized likelihood ratio. This presentation is rounded up by a comparative performance study based on extensive Monte Carlo simulations. An empirical illustration shows how the obtained results can be applied to real data.     

A Simple Minimum-Bias Percentile Estimator of the Location Parameter for the Gamma,Weibull, and Log-Normal Distributions

Estimating the unknown minimum (location) of a random variable has received some attention in the statistical literature, but not enough in the area of decision sciences. This is surprising, given that such estimation needs exist often in simulation and global optimization. This study explores the characteristics of two previously used simple percentile estimators of location. The study also identifies a new percentile estimator of the location parameter for the gamma, Weibull, and log-normal distributions with a smaller bias than the other two estimators. The performance of the new estimator, the minimum-bias percentile (MBP) estimator, and the other two percentile estimators are compared using Monte-Carlo simulation. The results indicate that, of the three estimators, the MBP estimator developed in this study provides, in most cases, the estimate with the lowest bias and smallest mean square error of the location for populations drawn from log-normal and gamma or Weibull (but not exponential) distributions. A decision diagram is provided for location estimator selection, based on the value of the coefficient of variation, when the statistical distribution is known or unknown.     

The Interpretation of Coefficients in Models with Qualitative Dependent Variables*

A substantial body of empirical accounting, finance, management, and marketing research utilizes single equation models with discrete dependent variables. Generally, the interpretation of the coefficients of the exogenous variables is limited to the sign and relative magnitude. This paper presents three methods of interpreting the coefficients in these models. The first method interprets the coefficients as marginal probabilities and the second method interprets the coefficients as elasticities of probability. The third method utilizes sensitivity analysis and examines the effect of hypothetical changes in exogenous variables on the probability of choice. This paper applies these methods to a published research study.