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Full-Text Articles in Business
Adaptive Appointment Systems With Patient Preferences, Wen-Ya Wang, D. Gupta
Adaptive Appointment Systems With Patient Preferences, Wen-Ya Wang, D. Gupta
Wen-Ya Wang
Patients' satisfaction with an appointment system when they attempt to book a nonurgent appointment is affected by their ability to book with a doctor of choice and to book an appointment at a convenient time of day. For medical conditions requiring urgent attention, patients want quick access to a familiar physician. For such instances, it is important for clinics to have open slots that allow same-day (urgent) access. A major challenge when designing outpatient appointment systems is the difficulty of matching randomly arriving patients' booking requests with physicians' available slots in a manner that maximizes patients' satisfaction as well as …
Enhancing The Communication Competency Of Business Undergraduates: A Consumer Socialization Perspective, K. C. Gehrt, M. O'Brien, David Mease
Enhancing The Communication Competency Of Business Undergraduates: A Consumer Socialization Perspective, K. C. Gehrt, M. O'Brien, David Mease
David Mease
Explaining how individuals acquire the necessary skills and knowledge to effectively participate in society is often accomplished through Socialization Theory. We investigate numerous socialization agents and their relationship with the communication competency of university business majors. Communication competency (reading, writing, and verbal) was measured via both a standardized skill test and self report. Exploratory analysis was conducted upon high and low communication competency groups that were identified via cluster analysis. Our findings generally indicate the most important socialization agents are via personal interactions whereas the least important socialization agents are influencing via primarily electronic or media-based methods.
Evidence Contrary To The Statistical View Of Boosting, David Mease, A. Wyner
Evidence Contrary To The Statistical View Of Boosting, David Mease, A. Wyner
David Mease
The statistical perspective on boosting algorithms focuses on optimization, drawing parallels with maximum likelihood estimation for logistic regression. In this paper we present empirical evidence that raises questions about this view. Although the statistical perspective provides a theoretical framework within which it is possible to derive theorems and create new algorithms in general contexts, we show that there remain many unanswered important questions. Furthermore, we provide examples that reveal crucial flaws in the many practical suggestions and new methods that are derived from the statistical view. We perform carefully designed experiments using simple simulation models to illustrate some of these …
Comment: Boosting Algorithms: Regularization, Prediction And Model Fitting, A. Buja, David Mease, A. Wyner
Comment: Boosting Algorithms: Regularization, Prediction And Model Fitting, A. Buja, David Mease, A. Wyner
David Mease
The authors are doing the readers of Statistical Science a true service with a well-written and up-to-date overview of boosting that originated with the seminal algorithms of Freund and Schapire. Equally, we are grateful for high-level software that will permit a larger readership to experiment with, or simply apply, boosting-inspired model fitting. The authors show us a world of methodology that illustrates how a fundamental innovation can penetrate every nook and cranny of statistical thinking and practice. They introduce the reader to one particular interpretation of boosting and then give a display of its potential with extensions from classification (where …
Boosted Classification Trees And Class Probability/Quantile Estimation, David Mease, A. Wyner, A. Buja
Boosted Classification Trees And Class Probability/Quantile Estimation, David Mease, A. Wyner, A. Buja
David Mease
The standard by which binary classifiers are usually judged, misclassification error, assumes equal costs of misclassifying the two classes or, equivalently, classifying at the 1/2 quantile of the conditional class probability function P[y = 1jx]. Boosted classification trees are known to perform quite well for such problems. In this article we consider the use of standard, off-the-shelf boosting for two more general problems: 1) classification with unequal costs or, equivalently, classification at quantiles other than 1/2, and 2) estimation of the conditional class probability function P[y = 1jx]. We first examine whether the latter problem, estimation of P[y = 1jx], …