Predictive Statistics

Analysis and Inference beyond Models

Bertrand S. Clarke University of Nebraska, Lincoln and Jennifer L. Clarke University of Nebraska, Lincoln

All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary ‘black box’ techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.

Part I. The Predictive View: 1. Why prediction?; 2. Defining a predictive paradigm; 3. What about modeling?; 4. Models and predictors: a bickering couple; Part II. Established Settings for Prediction: 5. Time series; 6. Longitudinal data; 7. Survival analysis; 8. Nonparametric methods; 9. Model selection; Part III. Contemporary Prediction: 10. Blackbox techniques; 11. Ensemble methods; 12. The future of prediction; References; Index.

Advance praise: 'Prediction, one of the most important practical applications of statistical analysis, has rarely been treated as anything more than an afterthought in most formal treatments of statistical inference. This important book aims to counter this neglect by a wholehearted emphasis on prediction as the primary purpose of the analysis. The authors cut a broad swathe through the statistical landscape, conducting thorough analyses of numerous traditional, recent, and novel techniques, to show how these are illuminated by taking the predictive perspective.' Philip Dawid,
University of Cambridge