Bayesian Estimation of Intensity Surfaces on the Sphere via Needlet Shrinkage and Selection

This paper describes an approach for Bayesian modeling in spherical datasets. Our method is based upon a recent construction called the needlet, which is a particular form of spherical wavelet with many favorable statistical and computational properties. We perform shrinkage and selection of needlet coefficients, focusing on two main alternatives: empirical-Bayes thresholding, and Bayesian local shrinkage rules. We study the performance of the proposed methodology both on simulated data and on two real data sets: one involving the cosmic microwave background radiation, and one involving the reconstruction of a global news intensity surface inferred from published Reuters articles in August, 1996. The fully Bayesian approach based on robust, sparse shrinkage priors seems to outperform other alternatives.Business Administratio

Nonparametric Bayesian multiple testing for longitudinal performance stratification

This paper describes a framework for flexible multiple hypothesis testing of autoregressive time series. The modeling approach is Bayesian, though a blend of frequentist and Bayesian reasoning is used to evaluate procedures. Nonparametric characterizations of both the null and alternative hypotheses will be shown to be the key robustification step necessary to ensure reasonable Type-I error performance. The methodology is applied to part of a large database containing up to 50 years of corporate performance statistics on 24,157 publicly traded American companies, where the primary goal of the analysis is to flag companies whose historical performance is significantly different from that expected due to chance.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS252 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem

This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens automatically in Bayesian analysis, and to distinguish this correction from the Bayesian Ockham's-razor effect. Our second goal is to contrast empirical-Bayes and fully Bayesian approaches to variable selection through examples, theoretical results and simulations. Considerable differences between the two approaches are found. In particular, we prove a theorem that characterizes a surprising aymptotic discrepancy between fully Bayes and empirical Bayes. This discrepancy arises from a different source than the failure to account for hyperparameter uncertainty in the empirical-Bayes estimate. Indeed, even at the extreme, when the empirical-Bayes estimate converges asymptotically to the true variable-inclusion probability, the potential for a serious difference remains.Comment: Published in at http://dx.doi.org/10.1214/10-AOS792 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Two-loop mass splittings in electroweak multiplets: winos and minimal dark matter

The radiatively-induced splitting of masses in electroweak multiplets is relevant for both collider phenomenology and dark matter. Precision two-loop corrections of $\mathcal{O}$(MeV) to the triplet mass splitting in the wino limit of the minimal supersymmetric standard model can affect particle lifetimes by up to $40\%$. We improve on previous two-loop self-energy calculations for the wino model by obtaining consistent input parameters to the calculation via two-loop renormalisation-group running, and including the effect of finite light quark masses. We also present the first two-loop calculation of the mass splitting in an electroweak fermionic quintuplet, corresponding to the viable form of minimal dark matter (MDM). We place significant constraints on the lifetimes of the charged and doubly-charged fermions in this model. We find that the two-loop mass splittings in the MDM quintuplet are not constant in the large-mass limit, as might naively be expected from the triplet calculation. This is due to the influence of the additional heavy fermions in loop corrections to the gauge boson propagators.Comment: 31 pages, 10 figures, 2 Table

China threat? Evidence from the WTO

The rise of China has elicited a voluminous response from scholars, business groups, journalists and beyond.Within this literature, a 'China Threat Theory' has emerged which portrays China as a destabilizing force within global politics and economics. Though originating in Realist accounts, this China Threat Theory has spread across to other approaches, and it increasingly forms the backdrop against which scholarly work positions itself. Our article contributes to this debate by examining China's role within the World Trade Organization (WTO). It assesses the extent to which China has been the disruptive power that it is often claimed to be. In particular, the article examines the change identified in Chinese diplomacy around 2008, and argues that this is attributable to the process of learning and socialization that China had to undergo as a new member, coupled with its elevation to a position of decision-making power. Contrary to the China Threat Theory, we find little to suggest that China has adopted an aggressive system challenging mode of behaviour. © 2013 Kluwer Law International BV, The Netherlands

Expectation-maximization for logistic regression

We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000). Indeed, our results allow a version of this variational-Bayes approach to be re-interpreted as a true EM algorithm. We study several interesting features of the algorithm, and of this previously unrecognized connection with variational Bayes. We also generalize the approach to sparsity-promoting priors, and to an online method whose convergence properties are easily established. This latter method compares favorably with stochastic-gradient descent in situations with marked collinearity