Predictive Statistics: Analysis and Inference beyond Models (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 46)

Clarke, Bertrand S.; Clarke, Jennifer L.

ISBN 10: 1107028280 / ISBN 13: 9781107028289

Edité par Cambridge University Press, 2018

A propos de cet article

Hardback book in nearly new condition with just a small publisher's 'damaged' stamp; even so not showing any defects, no splits, no cracks, no pen-marks, just some very minor surface rubbing. Contents are crisp, tight and fresh. Thus a never read book still in very presentable condition now offered for sale at a sensible price.

N° de réf. du vendeur 202875

Détails bibliographiques

• Titre: Predictive Statistics: Analysis and Inference beyond Models (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 46)
• Éditeur: Cambridge University Press
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 1107028280
• ISBN à 13 chiffres: 9781107028289
• Reliure: Hardcover
• Édition: First Edition.
• État de l'article: Like New

A propos de ce titre

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.

À propos des auteurs

Bertrand S. Clarke is Chair of the Department of Statistics at the University of Nebraska, Lincoln. His research focuses on predictive statistics and statistical methodology in genomic data. He is a fellow of the American Statistical Association, serves as editor or associate editor for three journals, and has published numerous papers in several statistical fields as well as a book on data mining and machine learning.

Jennifer Clarke is Professor of Food Science and Technology, Professor of Statistics, and Director of the Quantitative Life Sciences Initiative at the University of Nebraska, Lincoln. Her current interests include statistical methodology for metagenomics and prediction, statistical computation, and multitype data analysis. She serves on the steering committee of the Midwest Big Data Hub and is co-PI on an award from the NSF focused on data challenges in digital agriculture.