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Predictive Statistics: Analysis and Inference beyond Models: 46 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 46) - Hardcover
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A bold retooling of statistics to focus directly on predictive performance with traditional and contemporary data types and methodologies.
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About the Authors
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.
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Predictive Statistics: Analysis and Inference beyond Models (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 46)
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Cambridge University Press, 2018
ISBN 10: 1107028280
ISBN 13: 9781107028289
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Hardcover. Condition: Like New. First Edition. 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. Seller Inventory # 202875
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PREDICTIVE STATISTICS : ANALYSIS AND INFERENCE BEYOND MODELS
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Cambridge University Press, 2018
ISBN 10: 1107028280
ISBN 13: 9781107028289
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Predictive Statistics: Analysis and Inference beyond Models (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 46)
Published by
Cambridge University Press, 2018
ISBN 10: 1107028280
ISBN 13: 9781107028289
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Predictive Statistics : Analysis And Inference Beyond Models
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ISBN 10: 1107028280
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Predictive Statistics : Analysis And Inference Beyond Models
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ISBN 10: 1107028280
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Predictive Statistics: Analysi
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Predictive Statistics : Analysis and Inference Beyond Models
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Predictive Statistics: Analysis and Inference beyond Models (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 46)
Published by
Cambridge University Press, 2018
ISBN 10: 1107028280
ISBN 13: 9781107028289
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Predictive Statistics : Analysis And Inference Beyond Models
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Predictive Statistics (Hardcover)
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ISBN 10: 1107028280
ISBN 13: 9781107028289
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Hardcover. Condition: new. Hardcover. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781107028289
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