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Ecological StatisticsContemporary theory and application$
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Gordon A. Fox, Simoneta Negrete-Yankelevich, and Vinicio J. Sosa

Print publication date: 2015

Print ISBN-13: 9780199672547

Published to Oxford Scholarship Online: April 2015

DOI: 10.1093/acprof:oso/9780199672547.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in OSO for personal use (for details see www.oxfordscholarship.com/page/privacy-policy).date: 20 November 2018

Missing data

Missing data

mechanisms, methods, and messages

(p.81) 4 Missing data
Ecological Statistics

Shinichi Nakagawa

Oxford University Press

Missing data are ubiquitous in ecological and evolutionary data sets as in any other branch of science. The common methods used to deal with missing data are to delete cases containing missing data, and to use the mean to fill in missing values. However, these ‘traditional’ methods will result in biased estimation of parameters and uncertainty, and reduction in statistical power. Now, better missing data procedures such as multiple imputation and data augmentation are readily available and implementable. This chapter introduces the basics of missing data theory—most importantly, the three missing data mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR); the chapter also explains relevant concepts of importance such as EM algorithms and MCMC procedures. This chapter enables the application of proper missing data procedures, in particular multiple imputation, using R packages.

Keywords:   missing data, multiple imputation, data augmentation, complete case analysis, EM algorithms, MCMC, ignorable, MCAR, MAR, MNAR

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