MODEL‐BASED DIRECT ESTIMATION OF SMALL‐AREA DISTRIBUTIONS

Much of the small‐area estimation literature focuses on population totals and means. However, users of survey data are often interested in the finite‐population distribution of a survey variable and in the measures (e.g. medians, quartiles, percentiles) that characterize the shape of this distribution at the small‐area level. In this paper we propose a model‐based direct estimator (MBDE, Chandra and Chambers) of the small‐area distribution function. The MBDE is defined as a weighted sum of sample data from the area of interest, with weights derived from the calibrated spline‐based estimate of the finite‐population distribution function introduced by Harms and Duchesne, under an appropriately specified regression model with random area effects. We also discuss the mean squared error estimation of the MBDE. Monte Carlo simulations based on both simulated and real data sets show that the proposed MBDE and its associated mean squared error estimator perform well when compared with alternative estimators of the area‐specific finite‐population distribution function.     

SPC—making it work for the gas transportation business

Transco is the main provider of gas transportation to domestic and commercial customers in mainland Britain. Gas arrives in Britain at a steady rate but is consumed with a distinct diurnal pattern. The safe and timely movement of gas from arrival at the beach in various places in Britain to delivery at burners is the main driver for System Operations. The movement of gas is meticulously controlled and monitored resulting in a mass of information on pressure, flow and temperature. Gas is stored temporarily in various storage vessels and is moved around the pipes and in and out of storage as demand dictates. Demand is mostly dictated by the weather and is therefore subject to much variation. Transco and its predecessors have been transporting gas for over 50 years and are very successful as judged by their excellent safety record and the continual delivery of gas. Nevertheless, the company wished to improve itself and make further use of the many measurements collected. SPC is ideal for improving communication and understanding through increased visibility of data. All companies have special issues to face when they implement SPC, and this paper describes the way these were dealt with in System Operations and the lessons learnt along the way. The first part describes how performance measures were chosen for investigation. It includes a novel use of correlation between output and day-to-day conditions, which was successfully turned into a measure to check the uncheckable. The second part is about the issues involved with early application of SPC when features of the system are still unexplained. SPC has helped enhance understanding of the complex transportation process, encouraged team work, improved performance and provided an objective means of decision making.     

Linear Regression With Nested Errors Using Probability‐Linked Data

Probabilistic matching of records is widely used to create linked data sets for use in health science, epidemiological, economic, demographic and sociological research. Clearly, this type of matching can lead to linkage errors, which in turn can lead to bias and increased variability when standard statistical estimation techniques are used with the linked data. In this paper we develop unbiased regression parameter estimates to be used when fitting a linear model with nested errors to probabilistically linked data. Since estimation of variance components is typically an important objective when fitting such a model, we also develop appropriate modifications to standard methods of variance components estimation in order to account for linkage error. In particular, we focus on three widely used methods of variance components estimation: analysis of variance, maximum likelihood and restricted maximum likelihood. Simulation results show that our estimators perform reasonably well when compared to standard estimation methods that ignore linkage errors.     

A LINEAR MODEL WITH ERRORS LACKING A VARIANCE1

The problem discussed is that of estimating β= (β¹, …, β_k) in the model Y=βX +ε when X has a specified multivariate distribution and the error ε does not necessarily have a finite second moment, for example, ε symmetric stable. We construct a moment estimator based on the empirical characteristic function and establish asymptotic unbiassedness and normality. Most of the paper is concerned with the case when X is normal. Forms of the suggested estimator are given in (2.5), (4.6) and (5.5).     

Nothing is as Practical as a Good Theory: Bowen Theory and the Workplace — a Personal Application

Bowen Family Systems Theory is most commonly used to understand and predict family process. It is also applied to other potentially intense relationship systems, especially the workplace. It has been used by workplace consultants and by individuals to understand, and to manage, their own workplace functioning. This paper will draw from several key Bowen concepts to analyse common workplace dilemmas and to suggest responses that may assist the functioning of the individual and the system. The author's own experience as a manager in a child and adolescent mental health programme will be used as the case study to illustrate the theory's application.     

Preference Aggregation With Incomplete Information

We show in an environment of incomplete information that monotonicity and the Pareto property applied only when there is common knowledge of Pareto dominance imply (i) there must exist a common prior over the smallest common knowledge event, and (ii) aggregation must be ex ante and ex post utilitarian with respect to that common prior and individual von Neumann–Morgenstern utility indices.     

Local Utility Functions and Local Probability Transformations

This note shows that, under appropriate conditions, preferences may be locally approximated by the linear utility or risk-neutral preference functional associated with a local probability transformation.