TWO STAGE SELECTION PROBLEM FOR NORMAL POPULATIONS WITH UNEQUAL VARIANCES

The paper discusses the Dudewicz-Dalal modification of a Stein-type two sample procedure for the goal of selecting the population with the largest mean from k normal populations with unknown variances. Largest k values are obtained such that a procedure based on sample means is preferred to the Dudewicz-Dalal procedure. The more general goal of choosing those populations with the t largest means is also considered.     

An upper bound on a ratio of variances

Several processes may be monitored in terms of their variances relative to each other by the maximum ratio of variances. Based on the observed value an upper confidence bound for the population value is derived and tables are supplied for its implementation. Such bounds may be used when the experimenter wishes to establish that the variances are "not too different". The usual testing of variances for equality is noticed to be inappropriate in such cases.     

Global Studies—The Handmaiden of Neoliberalism?

Abstract

The field of global studies has gained momentum over the past 20 years and today occupies a significant presence within many universities. As a result, there is now a burgeoning array of institutional support for global studies scholarship. Perhaps not surprisingly, concurrent to such institution-building there has been a spate of essays engaged with the question ‘what is global studies?’ that have promoted lively debate and commentary. In this essay, I ask a rather different question which is ‘who gets to define global studies, and what is at stake in these kinds of delineating efforts?’ I argue that debate about global studies demands the inclusion of multiple voices and perspectives from around the world. I conclude by urging global studies scholars to be deliberately conscious of their taken-for-granted assumptions with respect to power and the related capacity to speak for others from the global south and east who are largely still absent in defining this new field of inquiry.     

Psychiatric Labelling and the Imputation of Mental Illness by Jurors

Abstract

Previous research suggests that jurors in defense-of-insanity trials are influenced significantly more by defendent behavior than by professional psychiatric diagnoses. This finding would appear to refute labelling theory's emphasis on societal reaction as equally or more important in the development of a deviant career. These earlier studies of jury decision making, however, were not designed as tests of the labelling perspective and the results must therefore be regarded as inconclusive. The study reported here represents an attempt to reassess the issue of the relative influence of defendent behavior and psychiatric labelling on jurors while allowing for conflicting testimony and operationalizing behavior in terms of residual rule-breaking. Defendent behavior again emerges as the variable with the greatest explanatory power, but the findings do provide evidence of a significant labelling effect as well. Implications for labelling theory and defense-of-insanity trials are discussed.     

COMPARISONS AND SELECTION OF TWO-PARAMETER EXPONENTIAL POPULATIONS

For two-parameter exponential populations with the same scale parameter (known or unknown) comparisons are made between the location parameters. This is done by constructing confidence intervals, which can then be used for selection procedures. Comparisons are made with a control, and with the (unknown) “best” or “worst” population. Emphasis is laid on finding approximations to the confidence so that calculations are simple and tables are not necessary. (Since we consider unequal sample sizes, tables for exact values would need to be extensive.)     

SELECTING THE t BEST POPULATIONS — SCALE PARAMETER CASE

Selection from k independent populations of the t (< k) populations with the smallest scale parameters has been considered under the Indifference Zone approach by Bechhofer k Sobel (1954). The same problem has been considered under the Subset Selection approach by Gupta & Sobel (1962a) for the normal variances case and by Carroll, Gupta & Huang (1975) for the more general case of stochastically increasing distributions. This paper uses the Subset Selection approach to place confidence bounds on the probability of selecting all “good” populations, or only “good” populations, for the Case of scale parameters, where a “good” population is defined to have one of the t smallest scale parameters. This is an extension of the location parameter results obtained by Bofinger & Mengersen (1986). Special results are obtained for the case of selecting normal populations based on variances and the necessary tables are presented.