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PHYSTAT 05

Abstracts

All abstracts are in Adobe PDF format


Random walks, a link between statistical, condensed matter, mathematical and particle Physics.
Ali Alavi (Seyed Ali Asghar Alavi)

Application of machine learning tools to particle Physics
P. Bargassa, S. Herrin, S-J Lee, P. Padley, R. Vilalta - Rice University and The University of Houston, USA

A note on Delta ln L = -1/2 errors
Roger Barlow, Physics.org

Asymmetric Errors
Roger Barlow, Physics.org

Bayesian Neural Networks
P.C. Bhat (Fermi, Il USA), H.B. Prosper (Florida State University)

Regularized inversion methods and error bounds for general statistical inverse problems with application to density estimation of young massive cluster luminosities in the Antennae galaxies
Dr. Nicolai Bissantz - University of Göttingen, Germany

Program for evaluation of the significance confidence intervals and limits by direct probabilities calculations
S. Bityukov and various - Institute for high energy physics, Russia

The Bayesian effects in measurement of the astmmetry of Poisson flows
S. Bityukov and various - Institute for high energy physics, Russia

Statistically dual distributions in statistical inference
S. Bityukov and various - Institute for high energy physics, Russia

A new fast track-fit algorithm based on broken lines
Volker Blobel - University of Hamburg, Germany

Sifting data in the real world
M.M. Block - Department of Physics & Astronomy, Northwestern University, IL USA.

Maximal information analysis: I - various Wayne State plots and the most common likelihood principle
G. Bonvicini, Wayne State University, Detroit

Least Squares Approach to the Alignment of the Generic High Precision Tracking System
P. Brückman de Renstrom, S. Haywood - Univeristy of Oxford and Rutherford Appleton Laboratory, UK

Statistics in ROOT
R. Brun, A. Kreshuk - CERN

CEDAR: Combined e-Science Data Analysis Resource
Andy Buckley - CEDAR, Durham, UK

Bias-Free Estimation in Multicomponent Maximum Likelihood Fits with Component-Dependent Templates
P. Catastini (Universita’ di Siena), G. Punzi (Scuola Normale Superiore) - INFN Pisa, Italy

Bayesian analysis at work: troublesome examples
J. Charles & Various - France, Germany.

Restoration of Supersymmetry against arbitrary small quantum corrections using feedforward neural network
Dr Ashish Chaturvedi

Likelihood Ratio Confidence Intervals with Bayesian Treatment of Systematic Uncertainties
J. Conrad (CERN) & F. Tegenfeldt (ISU)

Generalized Frequentist Methods for Particle Physics
Luc Demortier, The Rockefeller University

Bayesian Reference Analysis for Particle Physics
Luc Demortier, The Rockefeller University

Application of a multidimensional wavelet denoising algorithum for the detention and characterization of astrophysical sources of Gamma rays.
S.W.Digel, B.Zhang, J.Chiang, M.Fadili, J.-L.Starck

x2 test for comparison of weighted and unweighted histograms
N.D. Gagunashvili - University of Akureyri, Iceland

Unfolding with system identification
N.D. Gagunashvili - University of Akureyri, Iceland

How to do Bayes-Optimal Classification with Massive Datasets: Large-Scale Quasar Discovery
A. Gray and Various - School of Computer Science, Carnegie Mello University, USA

Goodness-of-Fit Statistics: Power Comparisons
M. Grazia Pia, B. Mascialino - INFN, Italy

An update on the Goodness-of-Fit Statistical Toolkit
M. Grazia Pia, B. Mascialino - INFN, Italy

The Bayesian Approach to Setting Limits: What to Avoid
Joel Heinrich - University of Pennsylvania

Examining the balance between optimising an analysis for best limit setting and best discovery potential
G. Hill, J. Hodges, B. Hughey and M. Stamatikos - University of Wisconsin, USA.

Likelihood analysis and goodness-of-fit for low count-rate experiments
A. Ianni - Laboratori Nazionali del Gran Sasso/INFN, Italy

Higher Criticism Statistic: Optimality and Applications in Cosmology and Astronomy
Jiashun Jin - Department of Statistics Purdue University, IN USA.

Expected principal component analysis of cosmic microwave background anisotropies
Samuel Leach, SISSA-ISAS, Italy
Additional Information: http://xxx.soton.ac.uk/abs/astro-ph/0506390

New Developments of ROOT Mathematical Software Libraries
Lorenzo Moneta - CERN

Confidence interval construction applied to an unfolding problem
K. Muenich, G. Hill, W. Rhode and H. Geenen

StatPatternRecongnition: A C++ Package for Statistical Analysis of High Energy Physics Data
Ilya Narsky - California Institute of Technology

Optimization of Signal Significance by Bagging Decision Trees
Ilya Narsky - California Institute of Technology

Fitting boundary value problems
Geoff Nicholls & Various - Department of Statistics, University of Oxford

sPLot; a statistical tool to unfold data distribitions
M. Pivk (Cern) and F.R. Le Diberder (LAL Paris University, France)

Ordering Algorithms and Confidence Intervals in the Presence of Nuisance Parameters
Giovanni Punzi - Scuola Normale Superiore and INFN - Pisa

A General Theory of Goodness of Fit in Likelihood Fits
Rajendran Raja - Fermi Nartional Accelerator Lab, Batavia, IL

Calculation of errors in fitted quantities in likelihood fits
Rajendran Raja - Fermi Nartional Accelerator Lab, Batavia, IL

The Boosting Technique for Particle Physics
B.P.Roe, H.Yang and J.Zhu - University of Michigan, USA.

Limits and Confidence Intervals in the Presence of Nuisance Parameters
Dr. Wolfgang Rolke, University of Puerto Rico - Mayaguez

Cosmological applications of Bayesian model selection techniques
Roberto Trotta - Oxford Astrophysics & Royal Astronomical Society

The RooFit toolkit for data modeling
W. Verkereke, NL

Signal Enhancement Using Multivariate Classification Techniques and Physical Constraints
R.Vilalta, P. Sarda and Others - Houston University and Rice University

Goodness-of-fit for sparce distributions in high energy physics.
B.D. Yabsley, University of Sydney, Australia

Maximum Likelihood Parameter Inference from Lowe Statistics Data and Monte Carlo Simulation
Dr Günter Zech, Germany

On Consistent and Calibrated Inference about the Parameters of Sampling Distributions
Tomi Zivko, Jozef Stefan Institute, Ljubljana, Slovenia

Statistical Software
Prof James Linneman, Michigan State University