Applications in Statistical Computing: From Music Data Analysis to Industrial Quality Improvement

Contents

• Part I Methodological Developments in Data Science.-Aviation Data Analysis by Linear Programming in Airline Network Revenue Management.- Bayesian Reduced Rank Regression for Classification.- Modelling and classification of GC/IMS breath gas measurements
• Part II Computational Statistics.- Heteroscedastic Discriminant Analysis using R.- Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco.-
• Part III Perspectives on Statistics and Data Science.- A Note on Artificial Intelligence and Statistics.- Statistical Computing and Data Science in Introductory Statistics.- Approaching Ethical Guidelines for Data Scientists.-
• Part IV Statistics in Econometric Applications.- Dating Lower Turning Points of Business Cycles – a Multivariate Linear Discriminant Analysis for Germany 1984 to 2009.- Partial Orderings of Default Predictions.- Improving GMM efficiency in dynamic models
• Part V Statistics in Industrial Applications.- Economically designed Bayesian np control charts using dual sample sizes for long-run processes.- Statistical analysis of the lifetime of diamond impregnated tools for core drilling of concrete.- Detection o
• Part VI Statistics in Music Applications.- Evaluation of Audio Feature Groups for the Prediction of Arousal and Valence in Music.- The Psychological Foundations of Classification.