??? 'Science with Gaia: how will we deal with a complex billion-source catalogue and data archive?' by Anthony Brown (Leiden University,Netherlads)
'Recent Advances in cosmological Bayesian model comparison' by Roberto Trotta (University College London, UK)
'The Art of Data Science' by Matthew Graham (Center for Advanced Computing Research, California Institute of Technology, USA)
'Astronomical Surveys: from SDSS to LSST' by Robert Lupton (Princeton University, USA)
'Exoplanet demography, quasar target selection, and probabilistic redshift estimation: Hierarchical models for density estimation, classification, and regression.' by David Hogg (New York University, USA)
'Learning to disentangle Exoplanet signals from correlated noise' by Suzanne Aigrain (Oxford University, UK)
Astroinformatics and data mining: how to cope with the data tsunami' by Giuseppe Longo (Federico II University, Italy)
Advanced statistical techniques for the processing of astronomical data: time series, images, low number statistics for high energy photons, heteroskedastic data, non-detections
Challenges in the data mining of astronomical databases: the class imbalance in training sets or how to define prior robust preprocessing for supervised/unsupervised classification robust inference with heterogeneous datasets, how to combine observations, models, priors, etc in a training/test set error propagation
The challenge of petabyte size databases: scalability, parallel computing, accuracy
Geometric data organization, sky indexing for efficient data retrieval, intelligent access to petabyte size databases
Knowledge Discovery in astronomical archives: outlier detection, new object types, parametric inference, model fitting and model selection, etc
Combining the classical domain knowledge approach with machine learning techniques
Global approaches for global datasets. The Galaxy zoo and the Universe zoo
The Virtual Observatories, Data Mining and Astrostatistics: software, standards, protocols
??? 'Science with Gaia: how will we deal with a complex billion-source catalogue and data archive?' by Anthony Brown (Leiden University,Netherlads)
'Recent Advances in cosmological Bayesian model comparison' by Roberto Trotta (University College London, UK)
'The Art of Data Science' by Matthew Graham (Center for Advanced Computing Research, California Institute of Technology, USA)
A GLR Control Chart for Monitoring the Process Variance by Marion R. Reynolds, Jr., and Jianying Lou
On the Robustness of the Shewhart Control Chart to Diffrent Types of Dependencies in Data by Olgierd
Assessing the Impact of Autocorrelation in Misleading Signals in Simultaneous Residual Schemes for the Process Mean and Variance: a Stochastic Ordering Approach by Patricia Ferreira Ramos, Manuel Cabral Morais, Antonio Pacheco and Wolfgang Schmid
More on Control Charting under Drift by Sven Knoth
Limit Properties of EWMA Charts for Stationary Processes by Manuel Cabral Morais, Yarema Okhrin, and Wolfgang Schmid
Economic Control Chart Policies for Monitoring Variables when there are Two Components of Variance by Erwin Saniga, James Lucas, Darwin Davis, and Thomas McWilliams
Process Monitoring Using an Online Nonlinear Data Reduction Based Control Chart by Issam Ben Khediri and Claus Weihs
On the Integration of SPC and APC: APC can be a Convenient Support for SPC by Ken Nishina, Masanobu Higashide, Hironobu Kawamura, and Naru Ishii
Process Adjustment Control Chart for Simultaneous Monitoring of Process Capability and State of Statistical Control by Hironobu Kawamura, Ken Nishina and Tomomichi Suzuki
Adaptive Threshold Methods for Monitoring Rates in Public Health Surveillance by Linmin Gan, William H. Woodall, and John L. Szarka
Spatiotemporal Bio Surveillance Under Non-homogeneous Population by Sung Won Han, Wei Jiang, and Kwok-Leung Tsui
Monitoring Hospital-Associated Infections with Control Charts by Christina M. Mastrangelo and Anna M. Gillan
Design and Implementation of Systems for Monitoring Lifetime Data by Emmanuel Yashchin
A Robust Detection Procedure for Multiple Change Points of Linear Trends by Seiichi Yasui, Hidehisa Noguchi and Yoshikazu Ojima
Risk-adjusted Cumulative Sum Charting Procedures by Fah F. Gan, Lin Lin, and Chok K. Loke
Bayesian Sampling Plans for Inspection by Variables by Peter-Th. Wilrich
Quality Assessment in the Presence of Additional Data in Photovoltaics by Sabine Meisen, Andrey Pepelyshev and Ansgar Steland
On Practical Uses of ISO Standards - Two Case Studies by Jürgen Iwersen
Part II Off-line Control: Hybrid Space-Filling Designs for Computer Experiments by Rachel T. Johnson, Douglas C. Montgomery and Kathryn S. Kennedy
Optimal Design for Multifactor Life Testing Experiments for Exponentially Distributed Lifetimes by Brandon R. Englert, Steven E. Rigdon, Connie M. Borror, Douglas C. Montgomery and Rong Pan
Accelerated Lifetime Testing of Thermal Insulation Elements by Rainer Göb, Kristina Lurz, and Ulrich Heinemann
Proposal of Advanced Taguchi's Linear Graphs for Split-Plot Experiments by Tomomichi Suzuki, Hironobu Kawamura, Seiichi Yasui and Yoshikazu Ojima
A Practical Variable Selection for Linear Models by Hidehisa Noguchi, Yoshikazu Ojima, and Seiichi Yasui
Capability of Detection for Poisson Distributed Measurements by Normal Approximations by Yusuke Tsutsumi, Hironobu Kawamura, and Tomomichi Suzuki
Business Data Quality Control - a Step by Step Procedure by Hans-J. Lenz and Esther Borowski
Data Quality: Algorithms for Automatic Detection of Unusual Measurements by Ross Sparks and Chris OkuGami
Uncertainty and Quality Control by Elart von Collani
Part I On-line Control
A GLR Control Chart for Monitoring the Process Variance by Marion R. Reynolds, Jr., and Jianying Lou
On the Robustness of the Shewhart Control Chart to Diffrent Types of Dependencies in Data by Olgierd
Joseph Hilbe, Jet Propulsion Laboratory and Arizona State University, Astrostatistics: A brief history and view to the future
Thomas Loredo, Cornell Univ, Bayesian astrostatistics: A backward look to the future
Stefano Andreon, INAF-Osservatorio Astronomico di Brera, Italy, Understanding better (some) astronomical data using Bayesian methods
Martin Kunz, Institute for Theoretical Physics, Univ of Geneva, BEAMS: separating the wheat from the chaff in supernova analysis
Benjamin Wandelt, Institut d'Astrophysique de Paris, Université Pierre et Marie Curie, France, Cosmostatistics
Roberto Trotta, Astrophysics Group, Dept of Physics, Imperial College London (with Farhan Feroz (Cambridge), Mike Hobson (Cambridge), and Roberto Ruiz de Austri (Univ of Valencia, Spain), Recent advances in Bayesian inference in cosmology and astroparticle physics thanks to the Multinest Algorithm
Phillip Gregory, Department of Astronomy, Univ of British Columbia, Canada, Extrasolar planets via Bayesian model fitting
Marc Henrion, Dept of Mathematics, Imperial College, London, UK (with Daniel Mortlock (Imperial), Axel Gandy (Imperial), and David J. Hand (Imperial)), Subspace methods for anomaly detection in high dimensional astronomical databases
Asis Kumar Chattopadhyay, Dept of Statistics, Univ of Calcutta, India (with Tanuka Chattyopadhyay, Tuli De, and Saptarshi Mondal), Independent Component Analysis for dimension reduction classification: Hough transform and CASH Algorithm
Marisa March, Astrophysics Group, Dept of Physics, Imperial College London (with Roberto Trotta), Improved cosmological constraints from a Bayesian hierarchical model of supernova type Ia data
Joseph Hilbe, Jet Propulsion Laboratory and Arizona State University, Astrostatistics: A brief history and view to the future
Thomas Loredo, Cornell Univ, Bayesian astrostatistics: A backward look to the future
Stefano Andreon, INAF-Osservatorio Astronomico di Brera, Italy, Understanding better (some) astronomical data using Bayesian methods
More Than a Dozen Alternative Ways of Spelling Gini
The Gini equivalents of the covariance, the correlation and the regression coefficient
Decompositions of the GMD
The Lorenz curve and the concentration curve
The extended Gini family of measures
Gini Simple Regressions
Multiple Regressions
Inference on Gini-based parameters -estimation
Inference on Gini-based parameters -testing
Inference on Lorenz and on Concentration curves
Introduction to applications
Social welfare, relative deprivation and the Gini coefficient
Policy Analysis.- Policy Analysis Using the Decomposition of the Gini by non-marginal analysis.- Incorporating poverty in Policy Analysis - the Marginal Analysis case
Introduction to applications of the GMD and the Lorenz curve in finance
The mean-Gini portfolio and the pricing of capital assets
Applications of Gini methodology in regression analysis
Gini's multiple regressions: two approaches and their interaction
Mixed OLS, Gini and extended Gini regressions.- An application in statistics - ANOGI
Suggestions for further research
Introduction
More Than a Dozen Alternative Ways of Spelling Gini
The Gini equivalents of the covariance, the correlation and the regression coefficient
Roles of Modeling in Statistical Inference.- Likelihood Construction and Estimation.- Likelihood-Based Tests and Confidence Regions.- Bayesian Inference.- Large Sample Theory: The Basics.- Large Sample Results for Likelihood-Based Methods.- M-Estimation (Estimating Equations).- Hypothesis Tests under Misspecification and Relaxed Assumptions .- Monte Carlo Simulation Studies .- Jackknife.- Bootstrap.- Permutation and Rank Tests.- Appendix: Derivative Notation and Formulas.- References.- Author Index.- Example Index
R-code Index
Subject Index.
Roles of Modeling in Statistical Inference.- Likelihood Construction and Estimation.- Likelihood-Based Tests and Confidence Regions.- Bayesian Inference.- Large Sample Theory: The Basics.- Large Sample Results for Likelihood-Based Methods.- M-Estimation (Estimating Equations).- Hypothesis Tests under Misspecification and Relaxed Assumptions .- Monte Carlo Simulation Studies .- Jackknife.- Bootstrap.- Permutation and Rank Tests.- Appendix: Derivative Notation and Formulas.- References.- Author Index.- Example Index
OPENSTAT REFERENCE MANUAL.- PREFACE.- TABLE OF CONTENTS
INTRODUCTION.- INSTALLING OPENSTAT.- STARTING OPENSTAT.- FILES.- CREATING A FILE.- ENTERING DATA.- SAVING A FILE.- DISTRIBUTIONS.- DESCRIPTIVE ANALYSES.- VERSUS Y PLOTS.- VERSUS MULTIPLE Y PLOT.- CORRELATION.- COMPARISONS.- MULTIVARIATE.- NON-PARAMETRIC.- MEASUREMENT.- STATISTICAL PROCESS CONTROL .- LINEAR PROGRAMMING.- THE ITEM BANKING PROGRAM.- NEURAL NETWORKS.- USING THE PROGRAM.- EXAMPLES
INDEX
OPENSTAT REFERENCE MANUAL.- PREFACE.- TABLE OF CONTENTS
INTRODUCTION.- INSTALLING OPENSTAT.- STARTING OPENSTAT.- FILES.- CREATING A FILE.- ENTERING DATA.- SAVING A FILE.- DISTRIBUTIONS.- DESCRIPTIVE ANALYSES.- VERSUS Y PLOTS.- VERSUS MULTIPLE Y PLOT.- CORRELATION.- COMPARISONS.- MULTIVARIATE.- NON-PARAMETRIC.- MEASUREMENT.- STATISTICAL PROCESS CONTROL .- LINEAR PROGRAMMING.- THE ITEM BANKING PROGRAM.- NEURAL NETWORKS.- USING THE PROGRAM.- EXAMPLES