Maintaining the Professional Integrity of Federal Statistics

Transactional analysis can be used to analyze personal interactions between consulting statisticians and their clients. The Parent-Adult-Child model has recently been refined to consist of six states by subdividing the Parent and Child ego states. The authors briefly define the six ego states (Nurturing Parent, Critical Parent, Adult, Adapted Child, Free Child, and Rebellious Child) and describe their occurrence during statistical consultations. Recommendations are given for identifying the ego states, setting realistic goals for relationships, and modifying unsatisfactory transactions.     

A Study of Student Reaction to the Use of Minitab in an Introductory Statistics Course

Traditionally, the introductory statistics course, Principles of Statistics (STAT 101), at Iowa State University has been taught without reference to a statistical analysis computing package. Although important for the implementation of statistical techniques, a computer component has been perceived by instructors to take time away from the coverage of statistical topics. To gauge students' reactions to the usefulness of a statistical computing package, an experiment was conducted during the fall term of 1986. Volunteers from a STAT 101 class were randomly assigned to either a control group or a computer use group. Both groups filled out questionnaires at the beginning and end of the semester. During the semester, the computer use group had access to and instruction in the use of Minitab. This instruction was tied to homework and laboratory assignments for the course. This article presents results of this experiment. On the basis of the responses to the questionnaires, the value of a statistical computing package as a pedagogical tool is examined. Recommendations for the use of a statistical computing package in a large introductory statistics course are made.     

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.     

Statistical Tools on the World Wide Web

The World Wide Web (WWW) represents a powerful tool for furthering the development and practice of statistics. The GASP (Globally Accessible Statistical Procedures) WWW site has been set up as a primary listing of statistical procedures which can be used over the WWW. This article highlights several possible approaches for making a procedure WWW accessible. These approaches effectively solve many of the problems typically encountered when using a new statistical procedure. Applying the methods discussed, any statistical technique can be made available to anyone with a forms- or Java-capable WWW browser. Procedures can be delivered in a virtually platform-independent manner with only minimal requirements on a user's hardware or software.     

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.     

Selecting the Optimal Transformation of a Continuous Covariate in Cox's Regression: Implications for Hypothesis Testing

ABSTRACT

Background: Many exposures in epidemiological studies have nonlinear effects and the problem is to choose an appropriate functional relationship between such exposures and the outcome. One common approach is to investigate several parametric transformations of the covariate of interest, and to select a posteriori the function that fits the data the best. However, such approach may result in an inflated Type I error. Methods: Through a simulation study, we generated data from Cox's models with different transformations of a single continuous covariate. We investigated the Type I error rate and the power of the likelihood ratio test (LRT) corresponding to three different procedures that considered the same set of parametric dose-response functions. The first unconditional approach did not involve any model selection, while the second conditional approach was based on a posteriori selection of the parametric function. The proposed third approach was similar to the second except that it used a corrected critical value for the LRT to ensure a correct Type I error. Results: The Type I error rate of the second approach was two times higher than the nominal size. For simple monotone dose-response, the corrected test had similar power as the unconditional approach, while for non monotone, dose-response, it had a higher power. A real-life application that focused on the effect of body mass index on the risk of coronary heart disease death, illustrated the advantage of the proposed approach. Conclusion: Our results confirm that a posteriori selecting the functional form of the dose-response induces a Type I error inflation. The corrected procedure, which can be applied in a wide range of situations, may provide a good trade-off between Type I error and power.     

Comments on the Mixture Detection Rule Used in SPC Control Charts

When calculating independently the false alarm rate of the eight usual runs rules used in SPC control chart, it appears that the proposed rule designed to detect mixture patterns corresponds to a Type-I error strongly lower than the seven other rules. This discrepancy is underlined and the mixture rule is showed to be useless both for in-control and out-of-control processes. Thus a modification of the mixture detection rule is proposed and the impact of this new mixture rule is then illustrated and discussed using Monte Carlo calculations.     

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.