Comparison of bias-reducing methods for estimating the parameter in dilution series

Ten different estimators of the parameter in a limiting or serial dilution assay are compared. Eight of them are constructed to reduce the bias of the commonly used maximum likelihood estimator. Extensive Monte Carlo experiments using various designs, and practical considerations, suggest that a particular jackknife version of the maximum likelihood estimator is preferred, provided that the design is not too small.     

Practitioners’ Personal and Professional Attitudes and Behaviors Toward Religion and Spirituality

This article presents findings from a study that examined the attitudes and behaviors toward religion and spirituality held by 328 randomly selected Virginia licensed clinical social workers, psychologists, and professional counselors. Significant differences were found among the three groups, with social workers generally holding a middle position in comparison with psychologists and professional counselors. As a whole, respondents were found to value the religious or spiritual dimension in their own lives, to respect the function it serves for people in general, and to address, to some extent, religious and spiritual issues in practice. Limited professional training in this area was reported, however, with 79% (n = 259) of the respondents stating that religious or spiritual issues were rarely or never addressed during the course of their graduate education and training. Implications for social work education and practice are discussed.     

A new characterization of Elfving's method for high dimensional computation

We give a new characterization of Elfving's (1952) method for computing c-optimal designs in k dimensions which gives explicit formulae for the k unknown optimal weights and k unknown signs in Elfving's characterization. This eliminates the need to search over these parameters to compute c-optimal designs, and thus reduces the computational burden from solving a family of optimization problems to solving a single optimization problem for the optimal finite support set. We give two illustrative examples: a high dimensional polynomial regression model and a logistic regression model, the latter showing that the method can be used for locally optimal designs in nonlinear models as well.     

A control chart for variance based on squared ranks

A nonparametric control chart for variance is proposed. The chart is constructed following the change-point approach through the recursive use of the squared ranks test for variance. It is capable of detecting changes in the behaviour of individual observations with performance similar to a self-starting CUSUM chart for scale when normality is assumed, and a relatively better power when assessing nonnormal observations. A comparison is also made with two equivalent nonparametric charts based on Mood and Ansari-Bradley statistics. When dealing with symmetrical distributions, the proposed chart shows smaller (better) out-of-control average run length (ARL), and a competing performance otherwise. In addition, sensitivity to changes in mean and variance at the same time was tested. Extensive Monte Carlo simulation was used to measure performance, and a practical example is provided to illustrate how the proposed control chart can be implemented in practice.     

Cool Pose: Black Male Homicide and the Social Implications of Manhood

ABSTRACT

Black male homicide has reached crisis proportions. This study investigated homicidal incidents to assess associations of Cool Pose with Black male homicides. Cool Pose is a tradition of manhood originating in Africa. The relationship of Cool Pose and homicide was examined by analyzing with multivariate statistical techniques 721 homicide incidents that occurred in the city of Newark, New Jersey. Findings suggest that Cool Pose is linked to homicides involving Black males. Society is not irrelevant to Cool Pose homicides and therefore relevant to their resolution. Social services may contribute to said resolution by informing the social work profession.     

Effect of forecast errors on rolling horizon master production schedule cost performance for various replanning intervals

Master production schedules are usually updated by the use of a rolling schedule. Previous studies on rolling schedules seem to form the consensus that frequent replanning of a master production schedule (MPS) can increase costs and schedule instability. Building on previous research on rolling schedules, this study addresses the impact of overestimation or underestimation of demand on the rolling horizon MPS cost performance for various replanning frequencies. The MPS model developed in this paper is based on actual data collected from a paint company. Results indicate that under both the forecast errors conditions investigated in this study, a two-replanning interval provided the best MPS cost performance for this company environment. However, results from the sensitivity analysis performed on the MPS model indicate that when the setup and inventory carrying costs are high, a 1-month replanning frequency (frequent replanning) seems more appropriate for both of the above forecast error scenarios.     

Response to Bonnie L. Yegidis

This article discusses distance learning techniques used to operationalize the concept of “globalization from below” in a social work methods course. In the E-Mail Partnership Project, one of the major course assignments, students communicate by e-mail to share information and discuss social issues through professional “pen pal” relationships with students overseas. The author reviews the course design, describes the project and its benefits, discusses the technical and cross-cultural issues that emerged throughout, and presents evaluation results Evaluation results suggest that international e-mail communication is one way to infuse international content into the curriculum.     

Robust analysis of variance

The classes of R- and M-estimates contain practical robust alternatives to least squares estimation in linear models. These estimates form the basis for a robust analysis of variance. This inference procedure is described and its versatility demonstrated.