9,346 research outputs found
Joining refractory/austenitic bimetal tubing Supplemental report
Joining bimetal tubing consisting of austenitic stainless steel with inner lining of niobium or tantalu
Incorporation of the statistical uncertainty in the background estimate into the upper limit on the signal
We present a procedure for calculating an upper limit on the number of signal
events which incorporates the Poisson uncertainty in the background, estimated
from control regions of one or two dimensions. For small number of signal
events, the upper limit obtained is more stringent than that extracted without
including the Poisson uncertainty. This trend continues until the number of
background events is comparable with the signal. When the number of background
events is comparable or larger than the signal, the upper limit obtained is
less stringent than that extracted without including the Poisson uncertainty.
It is therefore important to incorporate the Poisson uncertainty into the upper
limit; otherwise the upper limit obtained could be too stringent.Comment: 14 pages, 4 figure
Remarks for the Celebration of the Twentieth Anniversary of the Journal of Contemporary Health Law and Policy
The Ethical Review of Health Care Quality Improvement Initiatives: Findings From the Field
Based on surveys, examines the review mechanisms of quality improvement initiatives, including frequency; type, such as use of independent review boards; and consideration for ethical issues such as minimal risk and patient privacy and confidentiality
Harold Jeffreys's Theory of Probability Revisited
Published exactly seventy years ago, Jeffreys's Theory of Probability (1939)
has had a unique impact on the Bayesian community and is now considered to be
one of the main classics in Bayesian Statistics as well as the initiator of the
objective Bayes school. In particular, its advances on the derivation of
noninformative priors as well as on the scaling of Bayes factors have had a
lasting impact on the field. However, the book reflects the characteristics of
the time, especially in terms of mathematical rigor. In this paper we point out
the fundamental aspects of this reference work, especially the thorough
coverage of testing problems and the construction of both estimation and
testing noninformative priors based on functional divergences. Our major aim
here is to help modern readers in navigating in this difficult text and in
concentrating on passages that are still relevant today.Comment: This paper commented in: [arXiv:1001.2967], [arXiv:1001.2968],
[arXiv:1001.2970], [arXiv:1001.2975], [arXiv:1001.2985], [arXiv:1001.3073].
Rejoinder in [arXiv:0909.1008]. Published in at
http://dx.doi.org/10.1214/09-STS284 the Statistical Science
(http://www.imstat.org/sts/) by the Institute of Mathematical Statistics
(http://www.imstat.org
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