The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed. Front Matter....Pages i-viii Inferring Networks from High-Dimensional Data with Mixed Variables....Pages 1-15 Rounding Non-integer Weights in Bootstrapping Non-iid Samples: Actual Problem or Harmless Practice?....Pages 17-35 Measuring Downsize Reputational Risk in the Oil & Gas Industry....Pages 37-51 BarCamp: Technology Foresight and Statistics for the Future....Pages 53-67 Using Statistics to Shed Light on the Dynamics of the Human Genome: A Review....Pages 69-85 Information Theory and Bayesian Reliability Analysis: Recent Advances....Pages 87-102 (Semi-)Intrinsic Statistical Analysis on Non-Euclidean Spaces....Pages 103-118 An Investigation of Projective Shape Space....Pages 119-131 Treelet Decomposition of Mobile Phone Data for Deriving City Usage and Mobility Pattern in the Milan Urban Region....Pages 133-147 Methodological Issues in the Use of Administrative Databases to Study Heart Failure....Pages 149-160 Bayesian Inference for Randomized Experiments with Noncompliance and Nonignorable Missing Data....Pages 161-172 Approximate Bayesian Quantile Regression for Panel Data....Pages 173-189 Estimating Surfaces and Spatial Fields via Regression Models with Differential Regularization....Pages 191-209 The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S. Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. -- Back cover
