Inference under unequal probability sampling with the Bayesian exponentially tilted empirical likelihood.

Fully Bayesian inference in the presence of unequal probability sampling requires stronger structural assumptions on the data-generating distribution than frequentist semiparametric methods, but offers the potential for improved small-sample inference and convenient evidence synthesis. We demonstrate that the Bayesian exponentially tilted empirical likelihood can be used to combine the practical benefits of Bayesian inference with the robustness and attractive large-sample properties of frequentist approaches. Estimators defined as the solutions to unbiased estimating equations can be used to define a semiparametric model through the set of corresponding moment constraints. We prove Bernstein-von Mises theorems which show that the posterior constructed from the resulting exponentially tilted empirical likelihood becomes approximately normal, centred at the chosen estimator with matching asymptotic variance; thus, the posterior has properties analogous to those of the estimator, such as double robustness, and the frequentist coverage of any credible set will be approximately equal to its credibility. The proposed method can be used to obtain modified versions of existing estimators with improved properties, such as guarantees that the estimator lies within the parameter space. Unlike existing Bayesian proposals, our method does not prescribe a particular choice of prior or require posterior variance correction, and simulations suggest that it provides superior performance in terms of frequentist criteria.MR

MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference

MultiBUGS is a new version of the general-purpose Bayesian modeling software BUGS that implements a generic algorithm for parallelizing Markov chain Monte Carlo (MCMC) algorithms to speed up posterior inference of Bayesian models. The algorithm parallelizes evaluation of the product-form likelihoods formed when a parameter has many children in the directed acyclic graph (DAG) representation; and parallelizes sampling of conditionally-independent sets of parameters. A heuristic algorithm is used to decide which approach to use for each parameter and to apportion computation across computational cores. This enables MultiBUGS to automatically parallelize the broad range of statistical models that can be fitted using BUGS-language software, making the dramatic speed-ups of modern multi-core computing accessible to applied statisticians, without requiring any experience of parallel programming. We demonstrate the use of MultiBUGS on simulated data designed to mimic a hierarchical e-health linked-data study of methadone prescriptions including 425,112 observations and 20,426 random effects. Posterior inference for the e-health model takes several hours in existing software, but MultiBUGS can perform inference in only 28 minutes using 48 computational core

MultiBUGS: A Parallel Implementation of the BUGS Modelling Framework for Faster Bayesian Inference

MultiBUGS is a new version of the general-purpose Bayesian modelling software BUGS that implements a generic algorithm for parallelising Markov chain Monte Carlo (MCMC) algorithms to speed up posterior inference of Bayesian models. The algorithm parallelises evaluation of the product-form likelihoods formed when a parameter has many children in the directed acyclic graph (DAG) representation; and parallelises sampling of conditionally-independent sets of parameters. A heuristic algorithm is used to decide which approach to use for each parameter and to apportion computation across computational cores. This enables MultiBUGS to automatically parallelise the broad range of statistical models that can be fitted using BUGS-language software, making the dramatic speed-ups of modern multi-core computing accessible to applied statisticians, without requiring any experience of parallel programming. We demonstrate the use of MultiBUGS on simulated data designed to mimic a hierarchical e-health linked-data study of methadone prescriptions including 425,112 observations and 20,426 random effects. Posterior inference for the e-health model takes several hours in existing software, but MultiBUGS can perform inference in only 28 minutes using 48 computational cores

Happiness as a Driver of Risk-avoiding Behaviour: Theory and an Empirical Study of Seatbelt Wearing and Automobile Accidents

Governments try to discourage risky health behaviours, yet such behaviours are bewilderingly persistent. We suggest a new conceptual approach to this puzzle. We show that expected utility theory predicts that unhappy people will be attracted to risk-taking. Using US seatbelt data, we document evidence strongly consistent with that prediction. We exploit various methodological approaches, including Bayesian model selection and instrumental variable estimation. Using road accident data, we find strongly corroborative longitudinal evidence. Government policy may thus have to change. It may need to improve the underlying happiness of individuals instead of, or in addition to, its traditional concern with society's risk-taking symptoms. © 2014 The London School of Economics and Political Science

Assessing seismic origin of geological features by fitting equidistant parallel lines

Abstract Some planes in sedimentary rocks contain features that appear to lie near equally spaced parallel lines. Determining whether or not they do so can provide information on possible mechanisms for their formation. The problem is recast here in terms of circular statistics, enabling closeness of candidate sets of lines to the points to be measured by a mean resultant length. This leads to a test of goodness of fit and to estimates of the direction of the lines and of the spacing between them. Two contrasting data sets are analysed.</jats:p