Predictive Statistics: Analysis and Inference beyond Models by Clarke: New

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Book Title
Predictive Statistics: Analysis and Inference beyond Models
Publication Date
2018-04-12
ISBN
9781107028289
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
Cambridge University Press
ISBN-10
1107028280
ISBN-13
9781107028289
eBay Product ID (ePID)
242540115

Product Key Features

Number of Pages
656 Pages
Publication Name
Predictive Statistics : Analysis and Inference Beyond Models
Language
English
Publication Year
2018
Subject
Probability & Statistics / Stochastic Processes, Probability & Statistics / General
Type
Textbook
Author
Bertrand S. Clarke, Jennifer L. Clarke
Subject Area
Mathematics
Series
Cambridge Series in Statistical and Probabilistic Mathematics Ser.
Format
Hardcover

Dimensions

Item Height
1.6 in
Item Weight
46.9 Oz
Item Length
10.2 in
Item Width
7.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2017-045848
Reviews
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, Advance praise: 'The prime focus in statistics has always been on modeling rather than prediction; as a result, different prediction methods have arisen within different subfields of statistics, and a general, all-encompassing account has been lacking. For the first time, this book provides such an account and, as such, it convincingly argues for the primacy of prediction. The authors consider a wide range of topics from a predictive point of view and I am impressed by both the breadth and depth of the topics addressed and by the unifying story the authors manage to tell.' Peter Grnwald, Centrum Wiskunde & Informatica and Universiteit Leiden
Dewey Edition
23
Series Volume Number
Series Number 46
Illustrated
Yes
Dewey Decimal
519.54
Table Of Content
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.
Synopsis
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., Aimed at statisticians and machine learners, this retooling of statistical theory asserts that high-quality prediction should be the guiding principle of modeling and learning from data, then shows how. The fully predictive approach to statistical problems outlined embraces traditional subfields and 'black box' settings, with computed examples.
LC Classification Number
QA279.2

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