Vacuum energy density and pressure of a massive scalar field

With a view toward application of the Pauli-Villars regularization method to the Casimir energy of boundaries, we calculate the expectation values of the components of the stress tensor of a confined massive field in 1+1 space-time dimensions. Previous papers by Hays and Fulling are bridged and generalized. The Green function for the time-independent Schrodinger equation is constructed from the Green function for the whole line by the method of images; equivalently, the one-dimensional system is solved exactly in terms of closed classical paths and periodic orbits. Terms in the energy density and in the eigenvalue density attributable to the two boundaries individually and those attributable to the confinement of the field to a finite interval are distinguished so that their physical origins are clear. Then the pressure is found similarly from the cylinder kernel, the Green function associated most directly with an exponential frequency cutoff of the Fourier mode expansion. Finally, we discuss how the theory could be rendered finite by the Pauli-Villars method.Comment: 18 pages; v2 and v3 have minor improvement

Long-term effects of involuntary job separations on labour careers

In this article, we analyse whether involuntary job separations present long-term effects upon individuals' careers, and the magnitude of such effects. For this purpose, the impact of involuntary job separations on three measures of occupational prestige is examined, using the British Household Panel Survey. Involuntary job separations are found to show a negative effect upon those occupational prestige scales. In particular, when additional involuntary job separations are suffered, this negative impact is persistent and cumulative. Moreover, this observed decrease in prestige levels is enhanced by the length of job separations. Our results help to explain why displaced workers suffer persistent earnings losses compared to non-displaced workers along their work-life history

Predictions Based on the Clustering of Heterogeneous Functions via Shape and Subject-Specific Covariates

We consider a study of players employed by teams who are members of the National Basketball Association where units of observation are functional curves that are realizations of production measurements taken through the course of one's career. The observed functional output displays large amounts of between player heterogeneity in the sense that some individuals produce curves that are fairly smooth while others are (much) more erratic. We argue that this variability in curve shape is a feature that can be exploited to guide decision making, learn about processes under study and improve prediction. In this paper we develop a methodology that takes advantage of this feature when clustering functional curves. Individual curves are flexibly modeled using Bayesian penalized B-splines while a hierarchical structure allows the clustering to be guided by the smoothness of individual curves. In a sense, the hierarchical structure balances the desire to fit individual curves well while still producing meaningful clusters that are used to guide prediction. We seamlessly incorporate available covariate information to guide the clustering of curves non-parametrically through the use of a product partition model prior for a random partition of individuals. Clustering based on curve smoothness and subject-specific covariate information is particularly important in carrying out the two types of predictions that are of interest, those that complete a partially observed curve from an active player, and those that predict the entire career curve for a player yet to play in the National Basketball Association.Comment: Published at http://dx.doi.org/10.1214/14-BA919 in the Bayesian Analysis (http://projecteuclid.org/euclid.ba) by the International Society of Bayesian Analysis (http://bayesian.org/