Recapturing the Spirit of Furman: The American Bar Association and the New Abolitionist Politics

Sarat locates the American Bar Association\u27s call for a moratorium on executions in the context of modern abolitionist politics. New abolitionism links anti-death penalty work with a broader civil rights agenda

Recapturing the Spirit of Furman: The American Bar Association and the New Abolitionist Politics

Sarat locates the American Bar Association\u27s call for a moratorium on executions in the context of modern abolitionist politics. New abolitionism links anti-death penalty work with a broader civil rights agenda

Book Review

reviewing, Charles J. Ogletree, Jr. & Austin Sarat eds., When Law Fails: Making Sense of Miscarriages of Justice, (2009

Hierarchical mixture models for assessing fingerprint individuality

The study of fingerprint individuality aims to determine to what extent a fingerprint uniquely identifies an individual. Recent court cases have highlighted the need for measures of fingerprint individuality when a person is identified based on fingerprint evidence. The main challenge in studies of fingerprint individuality is to adequately capture the variability of fingerprint features in a population. In this paper hierarchical mixture models are introduced to infer the extent of individualization. Hierarchical mixtures utilize complementary aspects of mixtures at different levels of the hierarchy. At the first (top) level, a mixture is used to represent homogeneous groups of fingerprints in the population, whereas at the second level, nested mixtures are used as flexible representations of distributions of features from each fingerprint. Inference for hierarchical mixtures is more challenging since the number of unknown mixture components arise in both the first and second levels of the hierarchy. A Bayesian approach based on reversible jump Markov chain Monte Carlo methodology is developed for the inference of all unknown parameters of hierarchical mixtures. The methodology is illustrated on fingerprint images from the NIST database and is used to make inference on fingerprint individuality estimates from this population.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS266 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Regenerative Simulation for Queueing Networks with Exponential or Heavier Tail Arrival Distributions

Multiclass open queueing networks find wide applications in communication, computer and fabrication networks. Often one is interested in steady-state performance measures associated with these networks. Conceptually, under mild conditions, a regenerative structure exists in multiclass networks, making them amenable to regenerative simulation for estimating the steady-state performance measures. However, typically, identification of a regenerative structure in these networks is difficult. A well known exception is when all the interarrival times are exponentially distributed, where the instants corresponding to customer arrivals to an empty network constitute a regenerative structure. In this paper, we consider networks where the interarrival times are generally distributed but have exponential or heavier tails. We show that these distributions can be decomposed into a mixture of sums of independent random variables such that at least one of the components is exponentially distributed. This allows an easily implementable embedded regenerative structure in the Markov process. We show that under mild conditions on the network primitives, the regenerative mean and standard deviation estimators are consistent and satisfy a joint central limit theorem useful for constructing asymptotically valid confidence intervals. We also show that amongst all such interarrival time decompositions, the one with the largest mean exponential component minimizes the asymptotic variance of the standard deviation estimator.Comment: A preliminary version of this paper will appear in Proceedings of Winter Simulation Conference, Washington, DC, 201

Repo auction formats, bidders' behaviour and money market response in India

The treasury securities repo-auction is an important instrument for central banks in managing liquidity and sending interest rate signal to the money market. In the Indian context, the repo-auctions have been used actively in the post-reform period. The present study illustrates the money market reaction to repo-auctions and points out whether such reaction is consistent with applied auction rules. The policy implications are analysed in the light of alternative rules pertaining to discriminatory price auctions and fixed rate repos.Repo Auction Formats; Money Market Response

Inference for Differential Equation Models using Relaxation via Dynamical Systems

Statistical regression models whose mean functions are represented by ordinary differential equations (ODEs) can be used to describe phenomenons dynamical in nature, which are abundant in areas such as biology, climatology and genetics. The estimation of parameters of ODE based models is essential for understanding its dynamics, but the lack of an analytical solution of the ODE makes the parameter estimation challenging. The aim of this paper is to propose a general and fast framework of statistical inference for ODE based models by relaxation of the underlying ODE system. Relaxation is achieved by a properly chosen numerical procedure, such as the Runge-Kutta, and by introducing additive Gaussian noises with small variances. Consequently, filtering methods can be applied to obtain the posterior distribution of the parameters in the Bayesian framework. The main advantage of the proposed method is computation speed. In a simulation study, the proposed method was at least 14 times faster than the other methods. Theoretical results which guarantee the convergence of the posterior of the approximated dynamical system to the posterior of true model are presented. Explicit expressions are given that relate the order and the mesh size of the Runge-Kutta procedure to the rate of convergence of the approximated posterior as a function of sample size