Trends in the Race and Ethnicity of Eminent Americans

During the last several decades, the ethnic and racial composition of the American elite has changed to include some ethnic minorities and women. This study examines changes in the composition of one segment of the American elite: those who have obtained eminence in their occupations. Lieberson and Carter's study of the ethnic composition of eminent Americans, using Who's Who in America, is replicated with data from the 1990s (Lieberson and Carter, 1979, American Sociological Review 44:347–366). In addition, comparisons between blacks listed in Who's Who in America and blacks listed only in Who's Who among Black Americans are made. During the 20 years since Lieberson and Carter's study, Jews have made remarkable gains in eminent membership, while the rate of black representation has increased only moderately. Women are a small percentage of the eminent regardless of ethnicity, although black women are better represented than their counterparts in white ethnic groups.     

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.     

An application of catastrophe theory to management science process

Practicing management scientists often complain of the alleged irrational behavior on the part of the managers they are trying to serve, particularly when a manager suddenly and unexpectedly shifts behavior from one of support to one of resistance. The authors feel such behavior is only believed to be irrational because the management scientist is using the wrong mental model when projecting past behavioral patterns into the future. This paper attempts to solve this problem by using the newly developed catastrophe theory to develop a different model where sudden shifts in behavior are considered perfectly rational and explainable. The basic implication that is drawn from this new model is that successful implementation of management science depends on a sequence of interactions with the ultimate model user and that care in the structuring of these interactions can greatly enhance the probability of eventual user acceptance.     

Econometric analysis and managerial decision making

Econometric analysis is concerned with the quantitative relationships between economic variables and it can provide an important input into the decision making process of managers. Typically econometrics differs from other apsects of management science in that it considers problems primarily, though not exclusively, from a background of economics rather than of other disciplines and behaviour is usually dealt with at higher levels of data aggregation than the individual firm.This paper considers some applications of typical econometrics to the general area of managerial decision making, where primarily the techniques have a role to play in assisting the general process of data analysis. Initially discussion is pointed towards the use of the analysis for predictive purposes and the contrast with time series methods. Subsequently examples are presented where the objective is to obtain a better understanding of individual economic relationships that aim to be important inputs into the decision making process, for example cost and revenue analysis. Finally, an example is given of how these ideas contribute more generally to the activity of model building for the firm as a whole both for the purpose of forecasting and policy simulation.