Preview "Predictive Statistics" in a new window.

Predictive Statistics

Book Description

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.


In The Press


About the Author


Read on Your Favourite Devices

to find out more



Ebook Permissions

to find out more

About this Ebook

File formats
This ebook is available in:
The publisher has not yet supplied format information.
Pre-order formats shown are based on publisher intent and may change before release.
File sizes shown are an approximation. The actual download size will vary based on the application you use to read the book.
Publisher
Published
; Copyright:
ISBNs
Title
Series
Author
;
Edition
Imprint
Language
Number of Pages
Page count shown is an approximation provided by the publisher. The actual page count will vary based on various factors such as your device's screen size and font-size.