EVALUATING ALTERNATIVE LINEAR PROGRAMMING MODELS TO SOLVE THE TWO-GROUP DISCRIMINANT PROBLEM

The two-group discriminant problem has applications in many areas, for example, differentiating between good credit risks and poor ones, between promising new firms and those likely to fail, or between patients with strong prospects for recovery and those highly at risk. To expand our tools for dealing with such problems, we propose a class of nonpara-metric discriminant procedures based on linear programming (LP). Although these procedures have attracted considerable attention recently, only a limited number of computational studies have examined the relative merits of alternative formulations. In this paper we provide a detailed study of three contrasting formulations for the two-group problem. The experimental design provides a variety of test conditions involving both normal and nonnormal populations. Our results establish the LP model which seeks to minimize the sum of deviations beyond the two-group boundary as a promising alternative to more conventional linear discriminant techniques.     

Applications and Implementation

We propose an alternative solution to the discriminant problem, one that requires little more than a minimum familiarity with linear programming. The approach shows promise for eliminating the complexities of conventional statistical approaches without sacrificing the essential power of existing methods.     

Construal Processes in Preference Assessment

Interpreting people's preferences requires understanding how they have construed their tasks, interpreting the proposed alternatives in the context where the evaluation is being made. With stylized experimental or survey choices, researchers' challenge is typically identifying the features that people add in order to make their task real enough to answer (i.e., how they read between the lines). With rich “real world” choices, researchers' challenge is typically identifying the features that people neglect, as they reduce their task to manageable complexity (i.e., which lines they choose to read). In either case, if people misunderstand or mistrust the stated transaction, they may evaluate a different offer than the one that was proposed. Such misconstruals are a nuisance for investigators, insofar as dealing with them delays the measurements that motivated the research. However, they can also provide an opportunity, by focusing attention on how people give meaning to choice situations. This article describes procedures for studying construal processes, strategies for getting people to answer the questions that interest researchers, and options for interpreting responses when people construe questions differently than was intended.     

Hijacking Planes to Cuba: An Up-Dated Version of the Birthday Problem

Modern desk calculators compute distribution functions for many of the standard tabled distributions. Two such machines and some of their capabilities are discussed. Generally more is available from the calculators than is found in voluminous tables. One of the biggest advantages of the machines over tables arises from their capacity to compute probabilities for the two parameter F distribution, a set of values that is cumbersome to tabulate.     

COMPETITION AND COOPERATION IN A PUBLIC GOODS GAME: A FIELD EXPERIMENT

We explore the effects of competitive and cooperative motivations on contributions in a field experiment. A total of 10,000 potential political donors received solicitations referencing past contribution behavior of members of the competing party (competition treatment), the same party (cooperative treatment), or no past contribution information (control). We first theoretically analyze the effect of these treatments on the contribution behavior of agents with different social preferences in a modified intergroup public good (IPG) game. Then, we report the empirical results: Contribution rates in the competitive, cooperative, and control treatments were 1.45%, 1.08%, and 0.78%, respectively. With the exception of one large contribution, the distribution of contributions in the competitive treatment first order stochastically dominates that of the cooperative treatment. Qualitatively, it appears that the cooperative treatment induced more contributions around the common monetary reference point, while the competitive treatment led to more contributions at twice this amount. These results suggest that eliciting competitive rather than cooperative motivations can lead to higher contributions in IPG settings. (JEL D72, H41, C93)     

Data Mining and Knowledge Discovery in Databases – An Overview

Data mining seeks to extract useful, but previously unknown, information from typically massive collections of non-experimental, sometimes non-traditional data. From the perspective of statisticians, this paper surveys techniques used and contributions from fields such as data warehousing, machine learning from artificial intelligence, and visualization as well as statistics. It concludes that statistical thinking and design of analysis, as exemplified by achievements in clinical epidemiology, may fit well with the emerging activities of data mining and 'knowledge discovery in databases' (DM&KDD).     

Examples of sports-based youth development programs

The authors provide examples of sports-based youth development programs and offer information about program mission and vision, program design and content, evaluation results, and program sustainability. The four sports-based youth development programs presented are Harlem RBI, Tenacity, Snowsports Outreach Society, and Hoops & Leaders Basketball Camp. These programs serve diverse audiences with diverse missions, but all are focused on using sports to develop life skills and facilitate learning. Harlem RBI serves boys and girls ages seven to eighteen living in East Harlem. The program combines baseball, academic, and enrichment programs with the overall goal that participants who enter the program as vulnerable children graduate as resilient young adults. Tenacity, a nonprofit organization with headquarters in Boston, uses tennis to attract and retain students who particiate in a high-quality academic support and physical fitness program. The mission of Snowsports Outreach Society, based in Vail, Colorado, is building character in at-risk and underprivileged youth to develop their decision-making ability for healthy and successful life experiences. Hoops & Leaders Basketball Camp is a youth mentoring and leadership development program that offers summer camp experiences to improve the lives of at-risk urban youth in New York City. It uses the game of basketball to provide youth with caring mentors, develop leadership skills, and offer exposure to different educational and career paths.