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.
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