Factors Associated with the Development of Nonmetropolitan Growth Nodes in Producer Services Industries, 1980–1990*

Abstract Over the 1980–1990 period, employment in producer services industries in the nonmetropolitan United States increased substantially. This growth resulted in the development of nonmetropolitan growth nodes in producer services industries. A growth node refers to a nonmetropolitan area that contains a greater than average concentration of employment in a particular industry sector relative to other nonmetropolitan areas. Moreover, this industry concentration not only increases over time, but also represents an important source of employment growth within the area. With nonmetropolitan counties as the spatial unit of analysis, 317 growth nodes in producer services industries are identified for the 1980–1990 period. Access to workers with clerical and administrative support skills, access to highly educated workers, higher earnings per employed worker, access to recreational amenities, and proximity to metropolitan areas were associated with the development of nonmetropolitan growth nodes in producer services industries during this period.     

Review Essay: The Political Sociology of Nader and Chomsky

Sociological Forum -     

Local polynomial estimation of Poisson intensities in the presence of reporting delays

Summary.  The system for monitoring suicides in Hong Kong has considerable delays in reporting as the cause of death needs to be determined by a coroner's investigation. However, timely estimates of suicide rates are desirable to assist in the formulation of public health policies. This motivated us to develop a non-parametric procedure to estimate the intensity function of a Poisson process in the presence of reporting delays. We give closed form estimators of the Poisson intensity and the delay distribution, conduct simulation studies to evaluate the method proposed and derive their asymptotic properties. The method proposed is applied to estimate the intensity of suicide in Hong Kong.     

Plaid designs for the evaluation of training for medical practitioners

The training of medical practitioners to improve the practitioner/patient relationship may be difficult, as limitations often exist on the choice of patients included in the study. A specific study of this type of training is given. It is proposed that a simple modification and generalization of Yates' plaid-square designs be used. It is shown that a replicated plaid-design incorporates as a special case the criss-cross or strip-plot design. The usefulness of these designs in studies of the training of medical practitioners is illustrated. The basic characteristics of their analysis are outlined.     

Stochastic Modelling of Times to Temperature for Furnaces Supplying Titanium Blooms to a Rolling Mill at TIMET

In conjunction with TIMET at Waunarlwydd (Swansea, UK) a model has been developed that will optimise the scheduling of various blooms to their eight furnaces so as to minimise the time taken to roll these blooms into the finished mill products. This production scheduling model requires reliable data on times taken for the various furnaces that heat the slabs and blooms to reach the temperatures required for rolling. These times to temperature are stochastic in nature and this paper identifies the distributional form for these times using the generalised F distribution as a modelling framework. The times to temperature were found to be similarly distributed over all furnaces. The identified distributional forms were incorporated into the scheduling model to optimise a particular campaign that was run at TIMET Swansea. Amongst other conclusion it was found that, compared to the actual campaign, the model produced a schedule that reduced the makespan by some 35%.     

An alternative to model selection in ordinary regression

The weaknesses of established model selection procedures based on hypothesis testing and similar criteria are discussed and an alternative based on synthetic (composite) estimation is proposed. It is developed for the problem of prediction in ordinary regression and its properties are explored by simulations for the simple regression. Extensions to a general setting are described and an example with multiple regression is analysed. Arguments are presented against using a selected model for any inferences.     

Estimating ordered parameters by linear programming

There are many models that require the estimation of a set of ordered parameters. For example, multivariate analysis of variance often is formulated as testing for the equality of the parameters versus an ordered alternative. This problem, referred to as isotonic inference, constrained inference, or isotonic regression, has led to the development of general solutions, not often easy to apply in special models. In this expository paper, we study the special case of a separable convex quadratic programming problem for which the optimality conditions lead to a readily solved linear complementarity problem in the Lagrange multipliers, and subsequently to an equivalent linear programming problem, whose solution can be used to recover the solution of the original isotonic problem. The method can be applied to estimating ordered correlations, ordered binomial probabilities, ordered Poisson parameters, ordered exponential scale parameters, or ordered risk differences.