A Bayesian method for classification and discrimination

We discuss Bayesian analyses of traditional normal-mixture models for classification and discrimination. The development involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling, and demonstrates routine application. We stress the benefits of exact analyses over traditional classification and discrimination techniques, including the ease with which such analyses may be performed in a quite general setting, with possibly several normal-mixture components having different covariance matrices, the computation of exact posterior classification probabilities for observed data and for future cases to be classified, and posterior distributions for these probabilities that allow for assessment of second-level uncertainties in classification.     

Assessment of influence in non-linear measurement error models

We consider the assessment of outliers and influential observation in non-linear measurement error models. Residuals, leverage measures and case-deletiondiagonostics are examined. The method of local influence is also applied to the models. In particular, the perturbation of measurement error variances has been found useful in assessing the adequancy of the model assumptions. A numerical example is given to illustrate the application of the diagonostics.     

Low dose risk estimation via simultaneous statistical inferences

Summary.  The paper develops and studies simultaneous confidence bounds that are useful for making low dose inferences in quantitative risk analysis. Application is intended for risk assessment studies where human, animal or ecological data are used to set safe low dose levels of a toxic agent, but where study information is limited to high dose levels of the agent. Methods are derived for estimating simultaneous, one-sided, upper confidence limits on risk for end points measured on a continuous scale. From the simultaneous confidence bounds, lower confidence limits on the dose that is associated with a particular risk (often referred to as a bench-mark dose ) are calculated. An important feature of the simultaneous construction is that any inferences that are based on inverting the simultaneous confidence bounds apply automatically to inverse bounds on the bench-mark dose.     

Optimal orthogonal-array-based latin hypercubes

The use of optimal orthogonal array latin hypercube designs is proposed. Orthogonal arrays were proposed for constructing latin hypercube designs by Tang (1993). Such designs generally have better space filling properties than random latin hypercube designs. Even so, these designs do not necessarily fill the space particularly well. As a result, we consider orthogonal-array-based latin hypercube designs that try to achieve optimality in some sense. Optimization is performed by adapting strategies found in Morris & Mitchell (1995) and Ye et al. (2000). The strategies here search only orthogonal-array-based latin hypercube designs and, as a result, optimal designs are found in a more efficient fashion. The designs found are in general agreement with existing optimal designs reported elsewhere.     

Benchmark Analysis: Shopping with Proper Confidence

We discuss the issue of using benchmark doses for quantifying (excess) risk associated with exposure to environmental hazards. The paradigm of low-dose risk estimation in dose-response modeling is used as the primary application scenario. Emphasis is placed on making simultaneous inferences on benchmark doses when data are in the form of proportions, although the concepts translate easily to other forms of outcome data.     

Research project on advocacy and autism

This article presents the findings of a qualitative research project about the difficulties in accessing advocacy faced by adults labelled as having autism and Asperger's Syndrome in the Northwest of England. It is also an example of partnership working between three organisations. The article examines both the process of team-led (emancipatory) research and the project findings. Seven main themes emerged throughout the research: late diagnosis and lack of service support; bad experiences with systems of care; feelings of 'not belonging' (identity issues); barriers around communication and sensitivity towards individuals; lack of awareness and access to advocacy and rights; difficulties of 'fitting into' what is already available; and interest in developing knowledge around advocacy. Examination of the main themes pointed to a disturbing link between poor service response, episodes of crisis and mental ill health. The Carlisle People First Research Team is made up of 6 researchers who are labelled as having 'learning difficulties' who work in partnership with one other researcher.     

Avoiding responsibility: The politics and discourse of European development policy

This book deals with an important subject. Regrettably, it doesnot do so in a wholly successful way. The author's general purposeis to steer a path between a normative–ethical approachto the topic and a political–scientific one, and the mainmethodological stance adopted is a version of discourse analysis.Drawing, in particular, on the work of Laclau and Mouffe, sheexamines two discursive ‘moments’ in European developmentdiscourse, or two different ways of justifying European developmentpolicy – responsibility and efficiency. The first, atleast in the early stages of European policy, entailed a paternalisticsense of European concern for its former colonial possessions,while the latter legitimizes     

Population structure and correlation between auxiliary and target variables may affect precision of estimates in forest inventory

Simulations of forest inventory in several populations compared simple random with “quick probability proportional to size” (QPPS) sampling. The latter may be applied in the absence of a list sampling frame and/or prior measurement of the auxiliary variable. The correlation between the auxiliary and target variables required to render QPPS sampling more efficient than simple random sampling varied over the range 0.3–0.6 and was lower when sampling from populations that were skewed to the right. Two possible analytical estimators of the standard error of the estimate of the mean for QPPS sampling were found to be less reliable than bootstrapping.