There Goes the Neighborhood: How Racial Threat Shapes the Formation of Integrated and Segregated Neighborhoods

Neighborhoods in the United States are highly segregated by race. Black–white segregation is particularly acute. Although racial segregation has been declining in recent years, it remains extremely high—especially in the largest metropolitan areas. The historical pattern has been that as African Americans move into a neighborhood, whites move out and thus the neighborhood (gradually or rapidly) transitions from predominantly white to predominantly black. A seemingly contradictory pattern has recently been observed: integrated neighborhoods are becoming more common. Why do some neighborhoods transition from white to black and others become integrated? We use racial competition theory to answer this question. We examine the characteristics of stable, integrated neighborhoods and contrast them with segregated predominantly white, predominantly black, and rapidly transitioning neighborhoods in six large metropolitan areas. We find that the location of black neighborhoods is a key factor in whether an integrated neighborhood remains integrated. Specifically, we find integrated neighborhoods are spatially distant from majority black neighborhoods while transitioning neighborhoods are adjacent to them. Our findings support the “sense of threat” hypothesis of racial competition theory.     

Probability inequalities for continuous unimodal random variables with finite support

A paramecer-free Bernstein-type upper bound is derived for the probability that the sum S of n i.i.d, unimodal random variables with finite support, X₁ ,X₂,…,X_n, exceeds its mean E(S) by the positive value nt. The bound for P{S - nμ ≥ nt} depends on the range of the summands, the sample size n, the positive number t, and the type of unimodality assumed for X_i. A two-sided Gauss-type probability inequality for sums of strongly unimodal random variables is also given. The new bounds are contrasted to Hoeffding's inequality for bounded random variables and to the Bienayme-Chebyshev inequality. Finally, the new inequalities are applied to a classic probability inequality example first published by Savage (1961).     

Variable sampling intervals for multiparameter shewhart charts

This paper considers the problem of using control charts to simultaneously monitor more than one parameter with emphasis on simultaneously monitoring the mean and variance. Fixed sampling interval control charts are modified to use variable sampling intervals depending on what is being observed from the data. Two basic strategies are investigated. One strategy uses separate control charts for each parameter, A second strategy uses a proposed single combined statistic which is sensitive to shifts in both the mean and variance. Each procedure is compared to corresponding fixed interval procedures. It is seen that for both strategies the variable sampling interval approach is substantially more efficient than fixed interval procedures.     

The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities

The overlapping coefficient is defined as a measure of the agreement between two probability distributions. Its relationship to the dissimilarity index and its propertie are described. An extensive treatment of maximum-likelihood estimation of the overlap between two normal distributions is presented as an example of estimating the overlapping coefficient from sample data.     

Techniques for nonlinear least squares and robust regression

Recently, the authors and others have made considerable progress in developing algorithms for solving certain large-residual nonlinear least-squares problems where Gauss-Newton (GN) methods can be expected to perform poorly. These methods take account of the term in the Hessian ignored by the GN methods and use quasi-Newton procedures to update this term explicitly. This paper reviews these new approaches and discusses how they can be modified to give good performance on nonlinear models with robust loss functions where lack of scale invariance causes several new problems to arise.     

Estimation of a distribution function by extrapolating upper and lower bounds

Constrained optimization is proposed as a practical solution to the problem of estimating a distribution function at each point in a given set from monotone sequences of upper and lower bounds. The proposed solution employs least absolute value estimation and, hence, has a linear programming formulation. The special structure inherent in this formulation is exploited and an efficient computational method is discussed. The procedure is illustrated by two examples.     

An empirical bayes approach to a variables acceptance sampling plan problem

An empirical Bayes approach to a variables acceptance sampling plan problem is presented and an empirical Bayes rule is developed which is shown to be asymptotically optimal under general conditions. The problem considered is one in which the ratio of the costs of accepting defective items and rejecting non-defective items is specified. Sampling costs are not considered and the size of the sample taken from each lot is fixed and constant. The empirical Bayes estimation of the Bayes rule is shown to require the estimation of a conditional probability. An estimator for conditional probabilities of the form needed is derived and shown to have good asymptotic properties.     

Caliper pair-matching on a continuous variable in case-control studies

The use of matched pairs has been criticized as being less efficient than estimators based on random samples. This paper compares the mean square error of an analysis of covariance estimator based on random samples with two estimators based on caliper matched pairs. The first of these is a simple mean difference estimator and the second a regression estimator suggested by Rubin (1973b). Under conditions which commonly occur in epidemiologic case-control studies, both of the matched pair estimators can have smaller mean square errors than analysis o f covariance estimator. When there is a weak relationship between the matching and response variate, the mean difference estimator has a lower mean square error than the regression estimator.