The potential impact of discrimination fears of older gays, lesbians, bisexuals and transgender individuals living in small- to moderate-sized cities on long-term health care

As an exploration of the potential impact of fears of discrimination against GLBTs in long-term health care settings, this study compared perceptions of GLBT persons and heterosexuals. A total of 132 GLBT persons and 187 heterosexuals living in Eastern Washington completed a survey that contained demographic questions and perceptions of discrimination in long-term care settings. Most respondents suspected that staff and residents of care facilities discriminate against GLBTs. GLBT respondents who believed that residents of care facilities are victims of discrimination were more likely to believe that they would have to hide their sexual orientation if admitted to a care facility. GLBT respondents were more likely than heterosexual respondents to believe that GLBTs do not have equal access to health care and social services, that GLBTs residents of care facilities are victims of discrimination, that GLBT sensitivity training programs would benefit staff and residents of care facilities, and that GLBT retirement facilities would be a positive development for older GLBTs. This study is offered as a preliminary investigation of concerns about GLBT discrimination in health care settings, how concerns are expressed, and the implications of those concerns for health care needs.     

TEACHING EFFECTIVENESS IN THE DECISION SCIENCES: THE USE OF STUDENT EVALUATIONS

This article presents the results of an AIDS-sponsored investigation of teaching effectiveness in the decision sciences. Two national samples were employed in the study: a sample of 247 business school deans and a sample of 3,292 students from 32 different business schools. Included among the findings are that 87 percent of the deans responding indicate they require some sort of faculty teaching effectiveness evaluation, and that great care must be exercised when interpreting student ratings of an individual instructor's teaching effectiveness.     

A PLURALITY MAXIMIZING APPROACH TO THE ALLOCATION OF CAMPAIGN RESOURCES

An important problem in the planning of political campaigns is the allocation of resources to precincts and other geographical voting units in order to maximize a candidate's plurality. Essentially, parties may increase candidate plurality by engaging in registration drives, midcampaign activities such as canvassing and candidate appearances, and get-out-the-vote drives. Based on previous work which has related voter response to party identification, the authors develop a model for the optimal allocation of campaign resources to these activities. The computational feasibility of the solution procedure is made possible by a decomposition across activities.     

SENSITIVITY ANALYSIS WITH INTERACTION EFFECTS

The purpose of this paper is to extend the sensitivity analysis methodology as it applies to linear programming. The methodology developed herein is concerned with those changes in a technological coefficient that cause simultaneous changes in the relevant cost coefficients of the objective function. By proceeding from the definitions of feasibility and optimality, the authors develop formulas for determining bounds on the extent to which a technological coefficient, which has a predetermined effect on the corresponding cost coefficient, can vary without changing the optimal set of activities. After the formulas are developed, their use is demonstrated on a product-mix problem. This technique is of interest because changes in the rate at which an input is utilized can be expected to change the cost per unit of a resulting product. This interdependence should be recognized whenever a sensitivity analysis is performed on a technological coefficient of either a cost minimization or a contribution margin maximization problem.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

Using a mixture model for multiple imputation in the presence of outliers: the 'Healthy for life' project

Summary.  We consider the problem of obtaining population-based inference in the presence of missing data and outliers in the context of estimating the prevalence of obesity and body mass index measures from the 'Healthy for life' study. Identifying multiple outliers in a multivariate setting is problematic because of problems such as masking, in which groups of outliers inflate the covariance matrix in a fashion that prevents their identification when included, and swamping, in which outliers skew covariances in a fashion that makes non-outlying observations appear to be outliers. We develop a latent class model that assumes that each observation belongs to one of K unobserved latent classes, with each latent class having a distinct covariance matrix. We consider the latent class covariance matrix with the largest determinant to form an 'outlier class'. By separating the covariance matrix for the outliers from the covariance matrices for the remainder of the data, we avoid the problems of masking and swamping. As did Ghosh-Dastidar and Schafer, we use a multiple-imputation approach, which allows us simultaneously to conduct inference after removing cases that appear to be outliers and to promulgate uncertainty in the outlier status through the model inference. We extend the work of Ghosh-Dastidar and Schafer by embedding the outlier class in a larger mixture model, consider penalized likelihood and posterior predictive distributions to assess model choice and model fit, and develop the model in a fashion to account for the complex sample design. We also consider the repeated sampling properties of the multiple imputation removal of outliers.     

Quantifying the Distribution of Inhalation Exposure in Human Populations: 2. Distributions of Time Spent by Adults, Adolescents, and Children at Home, at Work, and at School

Using distributions of time spent at various ventilation levels, ranges of inhalation exposure in the population can be established. Distributions of exposure time were determined using results of a study by the California Air Resources Board (CARB) which focused on time spent by humans participating in various activities and the locations where the activities occurred. The daily at-home activities from the CARB study were assigned to one of three ventilation levels, generating aggregate time periods. Distinct age and gender populations were identified, and distributions for aggregate time were established for these populations at each of the ventilation levels. In addition to aggregate time spent at home, distributions for various ages and genders were established for aggregate time spent at school and work. By combining distributions of aggregate time with corresponding ventilation rates, the distribution of inhalation rates can be established for at home, at work, and at school exposures.     

Rewarding growth or profit?: Top management team compensation and governance in Japanese MNEs

Japanese multinational enterprises (MNEs) have been popularly characterized as pursuing competitive strategies emphasizing long-term performance, especially growth in sales and market share, rather than shorter term profit performance. Though prior research has emphasized the importance of linking compensation to organizational goals and performance, the relationship between performance and compensation of Japanese executives has received limited empirical attention. This paper provides a preliminary examination of how Japanese top management teams in the 106 largest industrial MNEs were compensated, particularly for performance on sales growth and profitability criteria, over the 1976–1993 time period. The relationships revealed between organizational performance and the bonus compensation of top management teams suggest that the emphasis of Japanese industrial MNEs regarding sales growth versus profitability may not be as simple as suggested by prior studies and popular characterizations.     

On perfect simulation for some mixtures of distributions

This paper studies the implementation of the coupling from the past (CFTP) method of Propp and Wilson (1996) in the set-up of two and three component mixtures with known components and unknown weights. We show that monotonicity structures can be exhibited in both cases, but that CFTP can still be costly for three component mixtures. We conclude with a simulation experiment exhibiting an almost perfect sampling scheme where we only consider a subset of the exhaustive set of starting values.