In studies of subjective well-being, economists and other researchers typically use a fixed or random effect estimation to control for unobservable heterogeneity across individuals. Such individual heterogeneity, although substantially reducing the estimated effect of many characteristics, is little understood. This paper shows that personality measures can account for 20% of this heterogeneity...

We discuss the use of fixed and random effects models in the context of educational research and set out the assumptions behind the two modelling approaches. To illustrate the issues that should be considered when choosing between these approaches, we analyse the determinants of pupil achievement in primary school, using data from the Avon Longitudinal Study of Parents and Children. We conclude...

An alternative to the classical mixedmodel with normal random effects is to use a Dirichlet process to model the random effects. Such models have proven useful in practice, and we have observed a noticeable variance reduction, in the estimation of the fixed effects, when the Dirichlet process is used instead of the normal. In this paper we formalize this notion, and give a theoretical justifica...

In this paper I discuss economic processes that may give rise to spatial patterns in data, and explore the relative merits of alternative modeling approaches when data are spatially correlated. Specifically, I present an estimation scheme that allows for spatial random effects, and focus attention on cases in which such a framework may be preferred to the more general fixed effects framework th...

New concepts in the manipulation of time in video editing are presented, enabling some unique visual effects. These effects include (i) slowing down (or postponing) some dynamic events while speeding up (or advancing) others. (ii) Creating dynamic panoramic images of dynamic scenes. (iii) Generating new views of a scene without a 3D model. (iv) Creating panoramic stereo images. Time manipulatio...

By taking the governing measure of a Dirichlet process as essentially the realization of a Dirichlet process itself, a hierarchical Dirichlet process emerges. This distribution enables the Bayesian treatment of two level hierarchical random effects models, with results paralleling those of Ferguson (1973).

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