A latent factor model for spatial data with informative missingness

A large amount of data is typically collected during a periodontal exam. Analyzing these data poses several challenges. Several types of measurements are taken at many locations throughout the mouth. These spatially-referenced data are a mix of binary and continuous responses, making joint modeling difficult. Also, most patients have missing teeth. Periodontal disease is a leading cause of tooth loss, so it is likely that the number and location of missing teeth informs about the patient's periodontal health. In this paper we develop a multivariate spatial framework for these data which jointly models the binary and continuous responses as a function of a single latent spatial process representing general periodontal health. We also use the latent spatial process to model the location of missing teeth. We show using simulated and real data that exploiting spatial associations and jointly modeling the responses and locations of missing teeth mitigates the problems presented by these data.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS278 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Complementary bound on W′W^\prime mass from Higgs to diphoton decay

Using the left-right symmetric model as an illustrative example, we suggest a simple and straightforward way of constraining the $W'$ mass directly from the decay of the Higgs boson to two photons. The proposed method is generic and applicable to a diverse range of models with a $W'$-boson that couples to the SM-like Higgs boson. Our analysis exemplifies how the precision measurement of the Higgs to diphoton signal strength can have a pivotal role in probing the scale of new physics.Comment: 8 pages, 2 figure

Bayesian nonparametric spatial modelling of ordinal periodontal data

none2noneDipankar Bandyopadhyay; Antonio CanaleDipankar, Bandyopadhyay; Canale, Antoni