Details

Autor(en) / Beteiligte

• Christie, Mike

Titel

Simplicity, complexity and modelling [electronic resource]

Link zum Volltext

• Wiley Online Library - AutoHoldings Books

Beschreibungen/Notizen

• Description based upon print version of record.
• Includes bibliographical references and index.
• Simplicity, Complexity and Modelling; Contents; Preface; Acknowledgements; Contributing authors; 1 Introduction; 1.1 The origins of the SCAM project; 1.2 The scope of modelling in the modern world; 1.3 The different professions and traditions engaged in modelling; 1.4 Different types of models; 1.5 Different purposes for modelling; 1.6 The purpose of the book; 1.7 Overview of the chapters; References; 2 Statistical model selection; 2.1 Introduction; 2.2 Explanation or prediction?; 2.3 Levels of uncertainty; 2.4 Bias-variance trade-off; 2.5 Statistical models; 2.5.1 Within-model inference
• 2.6 Model comparison 2.7 Bayesian model comparison; 2.7.1 Model uncertainty; 2.7.2 Laplace approximation; 2.8 Penalized likelihood; 2.8.1 Bayesian information criterion; 2.9 The Akaike information criterion; 2.9.1 Inconsistency of AIC; 2.10 Significance testing; 2.11 Many variables; 2.12 Data-driven approaches; 2.12.1 Cross-validation; 2.12.2 Prequential analysis; 2.13 Model selection or model averaging?; References; 3 Modelling in drug development; 3.1 Introduction; 3.2 The nature of drug development and scope for statistical modelling; 3.3 Simplicity versus complexity in phase III trials
• 3.3.1 The nature of phase III trials 3.3.2 The case for simplicity in analyzing phase III trials; 3.3.3 The case for complexity in modelling clinical trials; 3.4 Some technical issues; 3.4.1 The effect of co-variate adjustment in linear models; 3.4.2 The effect of co-variate adjustment in non-linear models; 3.4.3 Random effects in multi-centre trials; 3.4.4 Subgroups and interactions; 3.4.5 Bayesian approaches; 3.5 Conclusion; 3.6 Appendix: The effect of co-variate adjustment on the variance multiplier in least squares; References; 4 Modelling with deterministic computer models; 4.1 Introduction
• 4.2 Metamodels and emulators for computationally expensive simulators 4.2.1 Gaussian processes emulators; 4.2.2 Multivariate outputs; 4.3 Uncertainty analysis; 4.4 Sensitivity analysis; 4.4.1 Variance-based sensitivity analysis; 4.4.2 Value of information; 4.5 Calibration and discrepancy; 4.6 Discussion; References; 5 Modelling future climates; 5.1 Introduction; 5.2 What is the risk from climate change?; 5.3 Climate models; 5.4 An anatomy of uncertainty; 5.4.1 Aleatoric uncertainty; 5.4.2 Epistemic uncertainty; 5.5 Simplicity and complexity
• 5.6 An example: The collapse of the thermohaline circulation 5.7 Conclusions; References; 6 Modelling climate change impacts for adaptation assessments; 6.1 Introduction; 6.1.1 Climate impact assessment; 6.2 Modelling climate change impacts: From world development paths to localized impacts; 6.2.1 Greenhouse gas emissions; 6.2.2 Climate models; 6.2.3 Downscaling; 6.2.4 Regional/local climate change impacts; 6.3 Discussion; 6.3.1 Multiple routes of uncertainty assessment; 6.3.2 What is the appropriate balance between simplicity and complexity?; References
• 7 Modelling in water distribution systems
• 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
• English