Prof.
Leszek Roszkowski
(CERN and University of Sheffield)
We employ a Markov Chain Monte Carlo scanning technique and a Bayesian
analysis to perform efficient parameter inference of the CMSSM
(Constrained Minimal Supersymmetric Standard Model). The approach
allows us to vary simultaneously all the CMSSM parameters and relevant
Standard Model (nuisance) parameters, and to properly treat
experimental constraints from collider physics and...
James Linnemann
(Michigan State University)
A common practice in evaluating the contribution of various systematic uncertainties to a result is to evaluate the contribution of each uncertainty separately (typically changing the parameter by what are felt to be + and - one sigma of systematic uncertainty), then to add the induced changes in the result in quadrature. Statisticians would refer to this as "One Factor at a Time"...
Dr
Sergey Bityukov
(INSTITUTE FOR HIGH ENERGY PHYSICS, PROTVINO)
This report reviews new methodology for statistical inferences.
Point estimators, confidence intervals and $p-$values have been
fundamental tools for frequentist statisticians.
Confidence distributions, which can be viewed as ``distribution
estimators'', are often convenient devices for constructing all the
above statistical procedures plus more.
Dr
Alexey Drozdetskiy
(University of Florida, Gainesville, FL, USA)
Should an event excess compatible with the H->ZZ->4l decay
channel be observed at LHC, the statistical significance of the access
must be properly scaled down to account for the systematic errors and the
fact that the search is performed in a wide-open range of possible Higgs
boson masses. In this talk, we present results of studies addressing both
of the two contributions and show that...
James Linnemann
(Michigan State University)
It is possible to find event weighting schemes which produce parameter estimates with variance nearly the same as a ML estimate. But there are situations in which a full ML estimate is inconvenient, usually for computational reasons (iteration over large data sets for example). If an variable x associated with the events is a candidate discriminating variable (that is, its distribution for...
Mr
Andre Nepomuceno
(Federal University of Rio de Janeiro)
It is known that many interesting signals expected at LHC are of unknown shape and strongly
contaminated by background events. These signals will be difficult to
detect during the first years of LHC operation due to the initial low luminosity.
In this work, one proposes a method on how to obtain signal information of unknow shape from data even when there are very low signal and large...
Dr
Fredrik Tegenfeldt
(Iowa State University)
In high-energy physics, with the search for ever
smaller signals in ever larger data sets, it has
become essential to extract a maximum of the
available information from the data. Multivariate
classification methods based on machine learning
techniques have become a fundamental ingredient to
most analyses. Also the multivariate classifiers
themselves have significantly evolved in...
