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

Editore: Cambridge University Press, 2018

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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.

Codice articolo 202875

Dati bibliografici

• Titolo: Predictive Statistics: Analysis and Inference beyond Models (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 46)
• Editore: Cambridge University Press
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 1107028280
• ISBN 13: 9781107028289
• Rilegatura: Hardcover
• Edizione: First Edition.
• Condizione: Like New

Descrizione articolo

Riassunto

A bold retooling of statistics to focus directly on predictive performance with traditional and contemporary data types and methodologies.

Informazioni sugli autori

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.