An exact density-based empirical likelihood ratio test for paired data

The Wilcoxon rank-sum test and its variants are historically well-known to be very powerful nonparametric decision rules for testing no location difference between two groups given paired data versus a shift alternative. In this title, we propose a new alternative empirical likelihood (EL) ratio approach for testing the equality of marginal distributions given that sampling is from a continuous bivariate population. We show that in various shift alternative scenarios the proposed exact test is superior to the classic nonparametric procedures, which may break down completely or are frequently inferior to the density-based EL ratio test. This is particularly true in the cases where there is a nonconstant shift under the alternative or the data distributions are skewed. An extensive Monte Carlo study shows that the proposed test has excellent operating characteristics. We apply the density-based EL ratio test to analyze real data from two medical studies.     

Nonparametric AMOC Changepoint Tests for Stochastically Ordered Alternatives

The problem considered is that of testing on the basis of a finite sequence of independent observations if all the observations have the same distribution versus the alternative that there is a unique change in the distribution and i.i.d. observations after the change are stochastically larger. The distributions before and after the possible change are continuous but not fully specified. We suggest a family of nonparametric tests based on ranks. Asymptotic approximations for the significance level of the test are obtained analytically. Monte Carlo experiments show that the rate of convergence of our asymptotics is fast.     

Robust estimation methods for exponential data: a monte-carlo comparison

We compare the performance of seven robust estimators for the parameter of an exponential distribution. These include the debiased median and two optimally-weighted one-sided trimmed means. We also introduce four new estimators: the Transform, Bayes, Scaled and Bicube estimators. We make the Monte Carlo comparisons for three sample sizes and six situations. We evaluate the comparisons in terms of a new performance measure, Mean Absolute Differential Error (MADE), and a premium/protection interpretation of MADE. We organize the comparisons to enhance statistical power by making maximal use of common random deviates. The Transform estimator provides the best performance as judged by MADE. The singly-trimmed mean and Transform method define the efficient frontier of premium/protection.     

Controlling type 1 error rate for sequential,bioequivalence studies with crossover designs

Sample size reestimation in a crossover, bioequivalence study can be a useful adaptive design tool, particularly when the intrasubject variability of the drug formulation under investigation is not well understood. When sample size reestimation is done based on an interim estimate of the intrasubject variability and bioequivalence is tested using the pooled estimate of intrasubject variability, type 1 error inflation will occur. Type 1 error inflation is caused by the pooled estimate being a biased estimator of the intrasubject variability. The type 1 error inflation and bias of the pooled estimator of variability are well characterized in the setting of a two‐arm, parallel study. The purpose of this work is to extend this characterization to the setting of a crossover, bioequivalence study with sample size reestimation and to propose an estimator of the intrasubject variability that will prevent type 1 error inflation.     

People,Pipelines, and Probabilities: Clarifying Significance and Uncertainty in Environmental Impact Assessments

Determinations of significance play a pivotal role in environmental impact assessments because they point decision makers to the predicted effects of an action most deserving of attention and further study. Impact predictions are always subject to uncertainty because they rely on estimates of future consequences. Yet uncertainty is often neglected or treated in a perfunctory manner as part of the characterization, evaluation, and communication of anticipated consequences and their significance. Proposals to construct fossil fuel pipelines in North America provide a highly visible example; casual treatment of how uncertainty affects significance determinations has resulted in poorly informed stakeholders, frustrated industry proponents, and inconsistent choices on the part of public decision makers. Using environmental assessments for recent pipeline proposals as examples, we highlight five ways in which uncertainty is often neglected when determining impact significance and suggest that a mix of known methods, new guidelines, and appropriate oversight could greatly improve current practices.     

Contextualization and Context Theorizing in Teams Research: A Look Back and a Path Forward

In this paper we review recent field research on teams in the workplace with the objective to help advance research about teams and their external context. We investigate contextualization by reviewing the research settings in which teams have been studied, and we investigate context theorizing by reviewing work where external context variables have been explicitly modeled. We propose guidelines to improve contextualization and avenues to explore context theorizing.     

An Examination of Public Private Academic Partnerships: Does Program Success Enhance University Performance Outcomes?

In response to a steady decline in State funding, universities have become heavily involved in a variety of public private academic partnerships (PPAPs) in such areas as medical research, business incubation, technological commercialization, continuing professional education, and intercollegiate athletics. Despite this trend, research which examines if such partnerships actually enhance university funding is rare within the public organization literature. Given this, we focus on university intercollegiate athletics programs, which represent one of the largest PPAPs ever undertaken by universities, to examine whether PPAP success can potentially enhance university performance outcomes leading to increased funding levels. Such programs represent a PPAP because public, private, and academic entities all come together to form an organization (division 1 football at the bowl subdivision level) which seeks to benefit all three entities through sharing resources and responsibilities. Study results showed that athletic success positively influenced a variety of university performance outcome variables such as enrollments and endowments. While results indicate that PPAP success can enhance university funding, we also suggest modifications that should enhance the chances that PPAPs will be successful.     

THE USE OF CHILD-CENTERED PLAY THERAPY AND FILIAL THERAPY WITH HEAD START FAMILIES: A BRIEF REPORT

Play therapy and filial therapy show promise as effective ways to provide direct services to Head Start, addressing the needs of the children, the families, and the Head Start teachers and staff. This paper examines the utility of play and filial therapies for the Head Start population, presents a systemic explanation for the benefit of filial therapy, and provides a case example for illustration.     

A Note on Inconsistency of NPMLE of the Distribution Function from Left Truncated and Case I Interval Censored Data

We show that under reasonable conditions the nonparametric maximum likelihood estimate (NPMLE) of the distribution function from left-truncated and case 1 interval-censored data is inconsistent, in contrast to the consistency properties of the NPMLE from only left-truncated data or only interval-censored data. However, the conditional NPMLE is shown to be consistent. Numerical examples are provided to illustrate their finite sample properties.     

A linear model-based test for the heterogeneity of conditional correlations

Current methods of testing the equality of conditional correlations of bivariate data on a third variable of interest (covariate) are limited due to discretizing of the covariate when it is continuous. In this study, we propose a linear model approach for estimation and hypothesis testing of the Pearson correlation coefficient, where the correlation itself can be modeled as a function of continuous covariates. The restricted maximum likelihood method is applied for parameter estimation, and the corrected likelihood ratio test is performed for hypothesis testing. This approach allows for flexible and robust inference and prediction of the conditional correlations based on the linear model. Simulation studies show that the proposed method is statistically more powerful and more flexible in accommodating complex covariate patterns than the existing methods. In addition, we illustrate the approach by analyzing the correlation between the physical component summary and the mental component summary of the MOS SF-36 form across a fair number of covariates in the national survey data.