Using power transformations when approximating quantiles

Estimators are obtained tor quantiles of survival distributions. This is accomplished by approximating Lritr distribution of the transtorrneri data, where the transformation used is that of Box and Cox (1964). The normal approximation as in Box and Cox and, in addition, the extreme value approximation are considered. More generally, to use the methods given, the approximating distribution must come from a location-scale family. For some commonly used survival random variables T the performance of the above approximations are evaluated in terms of the ratio of the true quantiles of T to the estimated one, in the long run. This performance is also evaluated for lower quantiles using simulated lognormai, Weibull and gamma data. Several examples are given to illustrate the methodology herein, including one with actual data.     

Color codification: law,culture and the hue of communication

This paper explores the intersection between color, culture and the legal domain; it reveals how recent, and disturbing, developments in trademark law have allowed for corporate ownership of the powerful communicative media of color and discusses the implications of this (colorful) codification. Mapping the communication of color within our contemporary legal, political and social environment, the paper addresses how color's vibrant significatory power is hemmed in by law, by (legal) language and by corporations. Law, it is argued, stands as one of the primary and most powerful practical “tools” used to shape, standardize and contain contemporary communication – and currently laws governing trademark function to recode and constrain the presumably boundless media of color. This is troubling, since our colorful environment becomes simplified when the array of meanings attributed to a particular hue are narrowed and then granted legitimacy by the courts.     

Regression Modelling of Disease Risk in Relation to Point Sources

We describe a class of models for the investigation of possible raised risk of disease around putative sources of environmental pollution. An adaptation of the point process method suggested by Diggle and Rowlingson is presented, allowing the use of routinely available aggregated data and incorporating the more general distance–risk model suggested by Elliott and co-workers. An application to data on cancers of the stomach around municipal solid waste incinerators is presented.     

Markov bridges and enlarged filtrations

The semimartingale decomposition of a Markov chain, whose value at some future time is known, is obtained by considering an enlarged filtration.     

Using a mixture model for multiple imputation in the presence of outliers: the 'Healthy for life' project

Summary.  We consider the problem of obtaining population-based inference in the presence of missing data and outliers in the context of estimating the prevalence of obesity and body mass index measures from the 'Healthy for life' study. Identifying multiple outliers in a multivariate setting is problematic because of problems such as masking, in which groups of outliers inflate the covariance matrix in a fashion that prevents their identification when included, and swamping, in which outliers skew covariances in a fashion that makes non-outlying observations appear to be outliers. We develop a latent class model that assumes that each observation belongs to one of K unobserved latent classes, with each latent class having a distinct covariance matrix. We consider the latent class covariance matrix with the largest determinant to form an 'outlier class'. By separating the covariance matrix for the outliers from the covariance matrices for the remainder of the data, we avoid the problems of masking and swamping. As did Ghosh-Dastidar and Schafer, we use a multiple-imputation approach, which allows us simultaneously to conduct inference after removing cases that appear to be outliers and to promulgate uncertainty in the outlier status through the model inference. We extend the work of Ghosh-Dastidar and Schafer by embedding the outlier class in a larger mixture model, consider penalized likelihood and posterior predictive distributions to assess model choice and model fit, and develop the model in a fashion to account for the complex sample design. We also consider the repeated sampling properties of the multiple imputation removal of outliers.     

Estimation for hidden Markov random fields

A finite state Markov random field is noisily observed via a second finite state process. The parameters of the model are estimated, as well as the most likely signal given the observations.     

A MODEL FOR COLLABORATIVE TEAM PROCESSING IN BRIEF SYSTEMIC FAMILY THERAPY*

While there has been considerable growth in recent years in the use of collaborative teamwork in Systemic Family Therapy, there has been a dearth of literature concerning the process of team functioning. Based upon a careful analysis of actual teamwork, a model for conducting more efficient and effective team conferences has been developed. The ensuing discussion examines the risks in working in teams and proposes ways to counteract these. Further avenues of research are suggested.