One stage multiple comparisons of k-1 treatment mean lifetimes with the control for exponential distributions under heteroscedasticity

Abstract

In survival or reliability data analysis, it is often useful to estimate the quantiles of the lifetime distribution, such as the median time to failure. Different nonparametric methods can construct confidence intervals for the quantiles of the lifetime distributions, some of which are implemented in commonly used statistical software packages. We here investigate the performance of different interval estimation procedures under a variety of settings with different censoring schemes. Our main objectives in this paper are to (i) evaluate the performance of confidence intervals based on the transformation approach commonly used in statistical software, (ii) introduce a new density-estimation-based approach to obtain confidence intervals for survival quantiles, and (iii) compare it with the transformation approach. We provide a comprehensive comparative study and offer some useful practical recommendations based on our results. Some numerical examples are presented to illustrate the methodologies developed.     

Admissibility of Test Procedures for Linear Hypotheses in General Linear Model

This article shows that an F-test procedure is admissible for testing a linear hypothesis concerning one of the split mean vectors in a general linear model and an F-test procedure is also admissible for testing a linear hypothesis concerning another of the split mean vectors in the same model. These results are proved by showing that the critical functions of the tests are unique Bayes procedures with respect to proper prior distributions set in common for the null hypotheses and for the alternative ones, respectively.     

A Welcome to the International Statistical Conferences

Criteria for assessing the effectiveness of a medical screening program are difficult to define; medical knowledge and screening procedures change rapidly, and self-selection at medical screens is unavoidable. This article discusses these and other basic issues in evaluation of medical screening programs with particular reference to results from the HIP breast cancer study. In addition, the article reviews various statistical models that describe the processes of disease and screening. The models are shown to be statistically indistinguishable in practice because of the small sample sizes typically available in medical screening trials. Finally the article suggests incorporating knowledge from clinical trials and from studies of robustness into statistical models designed to identify reasonable strategies for screening.     

An Introduction to Ranking and Selection

This expository paper explains the philosophy of the indifference-zone approach and the subset-selection approach to ranking and selection procedures. It includes examples of operating characteristic curves and data applications for selection problems based on the binomial and normal distributions. A variety of different models and goals are provided with a list of references.     

Graphical Display of Means

A graphical procedure for the display of treatment means that enables one to determine the statistical significance of the observed differences is presented. It is shown that the widely used least significant difference and honestly significant difference statistics can be used to construct plots in which any two means whose uncertainty intervals do not overlap are significantly different at the assigned probability level. It is argued that these plots, because of their straightforward decision rules, are more effective than those that show the observed means with standard errors or confidence limits. Several examples of the proposed displays are included to illustrate the procedure.     

The Optimal Allocation Combination for the One-Sided Sequential Screening Procedure Based on the Individual Misclassification Error

In this article, the expected total costs of three kinds of quality cost functions for the one-sided sequential screening procedure based on the individual misclassification error are obtained, where the expected total cost is the sum of the expected cost of inspection, the expected cost of rejection, and the expected cost of quality. The computational formulas for three kinds of expected total costs are derived when k screening variables are allocated into r stages. The optimal allocation combination is determined based on the criterion of minimum expected total cost. At last, we give one example to illustrate the selection of the optimal allocation combination for the sequential screening procedure.     

Queering Canadian Social Work Accreditation standards and procedures: a content analysis

ABSTRACT

This paper reviews and critically analyzes the Canadian Association for Social Work Education—Association Canadienne Pour La Formation En Travail Social’s (CASWE-ACFTS’) Accreditation Standards and Procedures from a Sexual Orientation Gender Identity and Expression (SOGIESC) lens to examine how such are addressed in relevant documents. The CASWE-ACFTS Queer Caucus undertook a lead role in examining ‘diversity’ in the documents as it serves as an umbrella term for SOGIESC and other intersecting positionalities. This paper iterates an initial response in the CASWE-ACFTS Queer Caucus’ ongoing consultation with the CASWE-ACFTS’s Education Policy Committee’s work on Education Policy and Accreditation Standards 2019 (EPAS2019). The content analysis of the documents reveals that SOGIESC issues are not adequately addressed and this has implications for undergraduate and graduate social work curricula in Canada. The authors provide recommendations that address the integration of SOGIESC in CASWE-ACFTS’s Accreditation Standards and Procedures documents.     

Incremental Sampling Methodology: Applications for Background Screening Assessments

This article presents the findings from a numerical simulation study that was conducted to evaluate the performance of alternative statistical analysis methods for background screening assessments when data sets are generated with incremental sampling methods (ISMs). A wide range of background and site conditions are represented in order to test different ISM sampling designs. Both hypothesis tests and upper tolerance limit (UTL) screening methods were implemented following U.S. Environmental Protection Agency (USEPA) guidance for specifying error rates. The simulations show that hypothesis testing using two‐sample t ‐tests can meet standard performance criteria under a wide range of conditions, even with relatively small sample sizes. Key factors that affect the performance include unequal population variances and small absolute differences in population means. UTL methods are generally not recommended due to conceptual limitations in the technique when applied to ISM data sets from single decision units and due to insufficient power given standard statistical sample sizes from ISM.