Class,race, and the disposal of urban waste: Locations of landfills,incinerators, and sewage treatment plants

This study compared the socioeconomic status, racial composition, and ethnic composition of 49 randomly selected U.S. cities with those of the census tracts containing the solid waste disposal facilities and sewage treatment plants for those cities. Contrary to the environmental racism and classism hypotheses, residents of tracts with landfills or incinerators had higher incomes and were less likely to be minority group members than residents of the cities that generated the refuse—although they did have somewhat lower education levels. There were few differences between the population characteristics of the cities and the census tracts that contained their sewage treatment plants. Possible explanations for these findings are discussed.     

The servant identity: Influences on the cognition and behavior of servant leaders

This paper approaches the study of servant leadership by concentrating on the identity of servant leaders. An identity is important to one's sense of self and it influences the way a leader cognitively processes socially relevant information and exercises a particular leadership behavior in response to a situation. Unlike existing studies, which typically approach servant leadership as one of a number of possible leadership styles, and which merely describe its characteristics, this paper serves to explain the basis for individuals enacting this mode of leadership. This paper defines and elaborates on the servant identity by showing the psychological factors constituting it. Taking a socio-cognitive approach, the paper offers a theoretical framework for the servant identity. The framework encompasses the cognitive and behavioral disposition of leaders with servant identity and the organizational contexts that influence it. This new angle on servant leadership opens avenues for future research and practice.     

Estimation of mean using double sampling for stratification and multivariate auxiliary information

Several estimators for estimating the mean of a principal variable are proposed based on double sampling for stratification (DSS) and multivariate auxiliary information. The general properties of the proposed estimators are studied, search for optimum estimators is made and the proposed estimators are compared with the corresponding estimators based on unstratified double sampling (USDS).     

Recurrence relations for moments of different orders of extremes from gamma distribution

Certain recurrence relations for the moments of different orders of the largest order statistic from a gamma distribution with shape parameter p are obtained. By using this it is shown that for obtaining the moment of any order of each order statistic of a sample of size n from the gamma distribution, one has to evaluate at most n-2 single integrals.     

Automatic locally adaptive smoothing for tree-based set estimation

Tree-based methods similar to CART have recently been utilized for problems in which the main goal is to estimate some set of interest. It is often the case that the boundary of the true set is smooth in some sense, however tree-based estimates will not be smooth, as they will be a union of ‘boxes’. We propose a general methodology for smoothing such sets that allows for varying levels of smoothness on the boundary automatically. The method is similar to the idea underlying support vector machines, which is applying a computationally simple technique to data after a non-linear mapping to produce smooth estimates in the original space. In particular, we consider the problem of level-set estimation for regression functions and the dyadic tree-based method of Willett and Nowak [Minimax optimal level-set estimation, IEEE Trans. Image Process. 16 (2007), pp. 2965–2979].     

k-POD: A Method for k-Means Clustering of Missing Data

The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, are common in many applications. Mainstream approaches to clustering missing data reduce the missing data problem to a complete data formulation through either deletion or imputation but these solutions may incur significant costs. Our k-POD method presents a simple extension of k-means clustering for missing data that works even when the missingness mechanism is unknown, when external information is unavailable, and when there is significant missingness in the data.

[Received November 2014. Revised August 2015.]     

Determining the effects of intraclass correlation on factorial experiments

This paper considers the effects of the presence of eqmi-correlation between observations in a k-way factorial experiment, A technique to provide unbiased F-tests is also constructed.