177,325 research outputs found
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 matrix 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 record
expression levels for different genes, often highly correlated, while the
columns represent 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 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
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