THE REAL AND ILLUSORY VIRTUES OF ENTROPY-BASED MEASURES FOR BUSINESS AND ECONOMIC ANALYSIS

The entropy measure H=−σp_i log p_i is being used with increasing frequency in the analysis of business and economic data. It is, however, simply another measure of dispersion which can be related to the moments of the probability function. Its virtues stem from its decomposition and interpretative properties. This paper surveys the uses to which the measure has been put in the literature, and discusses whether its use has been appropriate and innovative.     

THE DETAIL SCHEDULING OF A PART-TIME WORK FORCE: A CASE STUDY OF TELLER STAFFING

This paper presents a comparison of two heuristic methods for assigning a part-time work force to meet varying daily work loads. The authors use actual operating data from Purdue National Bank, Lafayette, Indiana, to make teller staffing decisions. The performance criteria for comparison of the two heuristics is the minimum number of workers employed, minimum number of interbranch transfers, and computer time necessary to attain a solution.     

ON THE DESIGN OF HIERARCHICAL PRODUCTION PLANNING SYSTEMS

To provide effective managerial support for decisions related to production planning and scheduling processes, it is useful to partition the set of decisions into a hierarchical framework. In the resulting system, higher level decisions impose constraints on lower level actions, and lower level decisions provide the necessary feed-back to reevaluate higher level actions. The purpose of this paper is to suggest optimum procedures to deal with the resulting subproblems and to analyze the interaction mechanisms among the different hierarchical levels. Computational results are given.     

The impact of ethnicity on attitudes toward health care reform in New Mexico

Latinos tend to have significantly lower levels of access to general and top quality medical care than do non-Latino whites, and although disparities in access to health care have diminished for all other minority groups over time, they have widened for Latinos. Given these trends, current attempts to provide universal health care at both the national and state levels across the United States have large implications for the health status of Latinos. The objective of this analysis is to determine whether Latinos have different attitudes regarding health reform than non-Latino whites. Our data are from a statewide random digit dialing telephone survey of New Mexico residents, age 18 and older, conducted in the Fall of 2007. With a Latino population of 44% and ongoing health care reform efforts by the state legislature, New Mexico is an ideal location for this analysis. After controlling for a host of individual level factors, our findings suggest that while Latinos are less likely to identify health care as a salient state issue relative to the economy and crime, they are more likely than non-Latino whites to believe affordable health care programs are important. Finally, Latinos view employers, more than government or individuals, to be responsible for expanding health care coverage.     

Demand for health risk reductions: A cross-national comparison between the U.S. and Canada

Using a large stated preference survey conducted across the U.S. and Canada, we assess differences in individual willingness to pay (WTP) for health risk reductions between the two countries. Our utility-theoretic choice model allows for systematically varying marginal utilities for avoided future time in different adverse health states (illness-years, recovered/remission years, and lost life-years). We find significant differences between Canadian and U.S. preferences. WTP also differs systematically with age, gender, education, and marital status, as well as a number of attitudinal and subjective health-perception variables. Age profiles for WTP are markedly different across the two countries. Canadians tend to display flatter age profiles, with peak WTP realized at older ages.     

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.     

Generalized inferences on the common mean vector of several multivariate normal populations

The hypothesis testing and confidence region are considered for the common mean vector of several multivariate normal populations when the covariance matrices are unknown and possibly unequal. A generalized confidence region is derived using the concepts of generalized method based on the generalized p$p$-value. The generalized confidence region is illustrated with two numerical examples. The merits of the proposed method are numerically compared with those of existing methods with respect to their expected area or expected d-dimensional volumes and coverage probabilities under different scenarios.     

A simulation study of a discrete-time multiserver retrial queue with finite population

In this paper, we deal with a discrete-time multiserver retrial queue with finite population. Firstly, we study the Markov chain at the epochs immediately after slot boundaries making emphasis on the computation of its steady-state distribution. Then, the main performance measures are investigated. Besides, we simulate the waiting time of a customer in the retrial group under three different queueing policies. Some numerical examples are given to illustrate the analysis.     

Some theory and practical uses of trimmed L-moments

Trimmed L-moments, defined by Elamir and Seheult [2003. Trimmed L-moments. Comput. Statist. Data Anal. 43, 299–314], summarize the shape of probability distributions or data samples in a way that remains viable for heavy-tailed distributions, even those for which the mean may not exist. We derive some further theoretical results concerning trimmed L-moments: a relation with the expansion of the quantile function as a weighted sum of Jacobi polynomials; the bounds that must be satisfied by trimmed L-moments; recurrences between trimmed L-moments with different degrees of trimming; and the asymptotic distributions of sample estimators of trimmed L-moments. We also give examples of how trimmed L-moments can be used, analogously to L-moments, in the analysis of heavy-tailed data. Examples include identification of distributions using a trimmed L-moment ratio diagram, shape parameter estimation for the generalized Pareto distribution, and fitting generalized Pareto distributions to a heavy-tailed data sample of computer network traffic.