SUMMARYWe propose a class of kernel stick-breaking processes for uncountable collections of dependent random probability measures. The process is constructed by first introducing an infinite sequence of random locations. Independent random probability measures and beta-distributed random weights are assigned to each location. Predictor-dependent random probability measures are then constructed by mixing over the locations, with stick-breaking probabilities expressed as a kernel multiplied by the beta weights. Some theoretical properties of the process are described, including a covariatedependent prediction rule. A retrospective Markov chain Monte Carlo algorithm is developed for posterior computation, and the methods are illustrated using a simulated example and an epidemiological application.

Summary. This article considers Bayesian methods for density regression, allowing a random probability distribution to change flexibly with multiple predictors. The conditional response distribution is expressed as a nonparametric mixture of parametric densities, with the mixture distribution changing according to location in the predictor space. A new class of priors for dependent random measures is proposed for the collection of random mixing measures at each location. The conditional prior for the random measure at a given location is expressed as a mixture of a Dirichlet process (DP) distributed innovation measure and neighboring random measures. This specification results in a coherent prior for the joint measure, with the marginal random measure at each location being a finite mixture of DP basis measures. Integrating out the infinite-dimensional collection of mixing measures, we obtain a simple expression for the conditional distribution of the subject-specific random variables, which generalizes the Pólya urn scheme. Properties are considered and a simple Gibbs sampling algorithm is developed for posterior computation. The methods are illustrated using simulated data examples and epidemiologic studies.

A systematic investigation of conceptual colour associations. Associations with colours are a rich source of meaning and there has been considerable interest in understanding the capacity of colour to shape our functioning and behaviour as a result of colour associations. However, abstract conceptual colour associations have not been comprehensively investigated and many of the effects of colour on psychological functioning reported in the literature are therefore reliant on ad hoc rationalisations of conceptual associations with colour (e.g. blue-openness) to explain effects. In the present work we conduct a systematic, cross-cultural, mapping of conceptual colour associations using the full set of hues from the World Colour Survey (WCS). In Experiments 1a and 1b we explored the conceptual associations that English monolingual, Chinese bilingual and Chinese monolingual speaking adults have with each of the 11 Basic English Colour Terms (black, white, red, yellow, green, blue, brown, purple, pink, orange, grey). In Experiment 2 we determined which specific physical WCS colours are associated with which concepts in these three language groups. The findings reveal conceptual colour associations that appear to be 'universal' across all cultures (e.g. white-purity; bluewater/sky related; green-health; purple-regal; pink-'female' traits) as well as culture specific (e.g. red and orange-enthusiastic in Chinese; red-attraction in English). Importantly, the findings provide a crucial constraint on, and resource for, future work that seeks to understand the effect of colour on cognition and behaviour, enabling stronger a priori predictions about universal as well as culturally relative effects of conceptual colour associations on cognition and behaviour to be systematically tested.

In studies of properties of queues, for example in relation to Internet traffic, a subject that is of particular interest is the 'shape' of service time distribution. For example, we might wish to know whether the service time density is unimodal, suggesting that service time distribution is possibly homogeneous, or whether it is multimodal, indicating that there are two or more distinct customer populations. However, even in relatively controlled experiments we may not have access to explicit service time data. Our only information might be the durations of service time clusters, i.e. of busy periods. We wish to 'deconvolve' these concatenations, and to construct empirical approximations to the distribution and, particularly, the density function of service time. Explicit solutions of these problems will be suggested. In particular, a kernel-based 'deconvolution' estimator of service time density will be introduced, admitting conventional approaches to the choice of bandwidth. Copyright 2004 Royal Statistical Society.

When functional data come as multiple curves per subject, characterizing the source of variations is not a trivial problem. The complexity of the problem goes deeper when there is phase variation in addition to amplitude variation. We consider clustering problem with multivariate functional data that have phase variations among the functional variables. We propose a conditional subject-specific warping framework in order to extract relevant features for clustering. Using multivariate growth curves of various parts of the body as a motivating example, we demonstrate the effectiveness of the proposed approach. The found clusters have individuals who show different relative growth patterns among different parts of the body.