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Informationen zum Autor Stephen Senn Professor of Statistics, Department of Statistics, University of Glasgow. Senior Professor and Editor of the Wiley Statistics in Practice Series. His research concerns statistics applied to drug development, a subject he has written many papers on. Philip Dawid is a Professor in Statistics at Cambridge University. Researching fundamentally into the logical foundation of probability and statistics in terms of forecasting. He is currently an Editor of the journal Bayesian Analysis. Mike Christie is Professor of Reservoir Engineering at Heriot-Way University. His research interests lie in accurate numerical modeling of fluid flow, in porous media primarily. Andrew Cliffe Professor of Computational Applied Mathematics at the University of Nottingham. His research interests include fluid dynamics and nuclear waste disposal. Klappentext Several points of disagreement exist between different modelling traditions as to whether complex models are always better than simpler models, as to how to combine results from different models and how to propagate model uncertainty into forecasts. This book represents the result of collaboration between scientists from many disciplines to show how these conflicts can be resolved.Key Features:* Introduces important concepts in modelling, outlining different traditions in the use of simple and complex modelling in statistics.* Provides numerous case studies on complex modelling, such as climate change, flood risk and new drug development.* Concentrates on varying models, including flood risk analysis models, the petrol industry forecasts and summarizes the evolution of water distribution systems.* Written by experienced statisticians and engineers in order to facilitate communication between modellers in different disciplines.* Provides a glossary giving terms commonly used in different modelling traditions.This book provides a much-needed reference guide to approaching statistical modelling. Scientists involved with modelling complex systems in areas such as climate change, flood prediction and prevention, financial market modelling and systems engineering will benefit from this book. It will also be a useful source of modelling case histories. Zusammenfassung * Provides a thorough overview of the complex mathematical models involved in modeling complex processes * Written by experienced, respected and well established Statisticians and Engineers and designed to facilitate communication between modelers in different disciplines. Inhaltsverzeichnis Preface ix Acknowledgements xi Contributing authors xiii 1 Introduction 1 Mike Christie, Andrew Cliffe, Philip Dawid and Stephen Senn 1.1 The origins of the SCAM project 1 1.2 The scope of modelling in the modern world 2 1.3 The different professions and traditions engaged in modelling 3 1.4 Different types of models 3 1.5 Different purposes for modelling 5 1.6 The purpose of the book 6 1.7 Overview of the chapters 6 References 8 2 Statistical model selection 11 Philip Dawid and Stephen Senn 2.1 Introduction 11 2.2 Explanation or prediction? 12 2.3 Levels of uncertainty 12 2.4 Bias-variance trade-off 13 2.5 Statistical models 15 2.5.1 Within-model inference 16 2.6 Model comparison 18 2.7 Bayesian model comparison 18 2.7.1 Model uncertainty 19 2.7.2 Laplace approximation 20 2.8 Penalized likelihood 20 2.8.1 Bayesian information criterion 21 2.9 The Akaike information criterion 21 2.9.1 Inconsistency of AIC 23 2.10 Significance testing 23 2.11 Many variables 27 2.12 Data-driven approaches 28 2.12.1 Cross-validation 29 2.12.2 Prequential analysis 29 2.13 Model selection or model averag...
