The prophecy

Everything about Kaleem marks him out from the rest: the blond hair and the dark skin, the humble cave where he lives and the fact that he doesn't know his father. He's used to unwelcome attention, but even so, he'd feel better if some strange old man didn't keep following him around. Then the man introduces himself and begins to explain the Babel Prophecy - and everything in Kaleem's life changes forever

Kiters

Robbie is a little disappointed when he gets a home-made kite for his birthday. He would really have liked a computer. But then he gets more than he bargained for with his new kite. Can it help him, though, to find out what is killing the fish and the seabirds and making the sea lose its sparkle? Will Osman, the sleeping giant who lies beneath the Gull Cliff, be able to help? And what about the seagulls who are always hanging around when Robbie and best friend Jace fly their kites? Or the strange old fisherman who seems to be dumping something nasty-looking into the sea? Robbie and Jace are swept into a world of danger, threat and magic as they try to find out what is spoiling their beautiful Gull Cliff Bay

Babel

Kaleem has found his father and soon finds the love of his life, Rozia Laurence, but he is still not comfortable with his role as peace Child. He also has to face some of the less palatable truths about his home planet: it is blighted by the existence of the Z Zone, a place where people live outside of society, and by switch-off, compulsory euthanasia for a healthy but aging population, including his mentor, Razjosh. Kaleem knows he can and must make a difference, but at what personal cost

Nick's gallery

Barney is having a hard time. His best friend Nick is dying; he has to compete for the county swimming championships; he has school work to contend with; he is just getting to know his first girlfriend; he is coping with his first part-time job. Everything is just about holding together … then what he has been dreading most happens and all sorts of other things go wrong as well. This is the moving story of how Barney comes to terms with his grief over his friend Nick. We share his pain and eventually his hopes for the future

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

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