Morality Grounds Personal Identity

There is a connection between moral facts and personal identity facts: morality grounds personal identity. If, for example, old Sally enters a teletransporter, and new Sally emerges, the fundamental question to ask is: is new Sally morally responsible for actions (and omissions) of old Sally? If the moral facts are such that she is morally responsible, then Sally persisted through the teletransporter event, and if not, Sally ceased to exist

Book Review: Gurus in America

A review of Gurus in America edited by Thomas A. Forsthoefel and Cynthia Ann Humes

Editor\u27s Introduction

The editor\u27s introduction to this issue

Rejoinder: Microarrays, Empirical Bayes and the Two-Groups Model

Rejoinder to ``Microarrays, Empirical Bayes and the Two-Groups Model'' [arXiv:0808.0572]Comment: Published in at http://dx.doi.org/10.1214/08-STS236REJ the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Microarrays, Empirical Bayes and the Two-Groups Model

The classic frequentist theory of hypothesis testing developed by Neyman, Pearson and Fisher has a claim to being the twentieth century's most influential piece of applied mathematics. Something new is happening in the twenty-first century: high-throughput devices, such as microarrays, routinely require simultaneous hypothesis tests for thousands of individual cases, not at all what the classical theory had in mind. In these situations empirical Bayes information begins to force itself upon frequentists and Bayesians alike. The two-groups model is a simple Bayesian construction that facilitates empirical Bayes analysis. This article concerns the interplay of Bayesian and frequentist ideas in the two-groups setting, with particular attention focused on Benjamini and Hochberg's False Discovery Rate method. Topics include the choice and meaning of the null hypothesis in large-scale testing situations, power considerations, the limitations of permutation methods, significance testing for groups of cases (such as pathways in microarray studies), correlation effects, multiple confidence intervals and Bayesian competitors to the two-groups model.Comment: This paper commented in: [arXiv:0808.0582], [arXiv:0808.0593], [arXiv:0808.0597], [arXiv:0808.0599]. Rejoinder in [arXiv:0808.0603]. Published in at http://dx.doi.org/10.1214/07-STS236 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Are a set of microarrays independent of each other?

Having observed an $m\times n$ matrix $X$ whose rows are possibly correlated, we wish to test the hypothesis that the columns are independent of each other. Our motivation comes from microarray studies, where the rows of $X$ record expression levels for $m$ different genes, often highly correlated, while the columns represent $n$ individual microarrays, presumably obtained independently. The presumption of independence underlies all the familiar permutation, cross-validation and bootstrap methods for microarray analysis, so it is important to know when independence fails. We develop nonparametric and normal-theory testing methods. The row and column correlations of $X$ interact with each other in a way that complicates test procedures, essentially by reducing the accuracy of the relevant estimators.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS236 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org