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Multiple imputation and its applicat...
~
Carpenter, James R.
Multiple imputation and its application /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Multiple imputation and its application /James R. Carpenter and Michael G. Kenward.
作者:
Carpenter, James R.
其他作者:
Kenward, Michael G.,
出版者:
Chichester, West Sussex :John Wiley & Sons,2013.
面頁冊數:
xviii, 345 p. :ill. ;24 cm.
標題:
Data Interpretation, Statistical.
ISBN:
9780470740521 (hbk.) :
Multiple imputation and its application /
Carpenter, James R.
Multiple imputation and its application /
James R. Carpenter and Michael G. Kenward. - 1st ed. - Chichester, West Sussex :John Wiley & Sons,2013. - xviii, 345 p. :ill. ;24 cm. - Statistics in practice.
Includes bibliographical references and indexes.
Machine generated contents note: Reasons for missing data Summary -- The Multiple Imputation Procedure and Its Justification -- Multiple imputation of binary and ordinal data -- Imputation of unordered categorical data -- Non-linear relationships -- Interactions -- Survival data, skips and large datasets -- Multilevel multiple imputation -- Sensitivity analysis: MI unleashed -- Including survey weights -- Robust Multiple Imputation.
"This book is written with three main aims; to provide a thorough introduction to the general MI methods, to provide a detailed discussion of the practical use of the MI method and to present real-world examples drawn from the field of biostatistics"--Provided by publisher.
ISBN: 9780470740521 (hbk.) :NT$1952
LCCN: 2012028821Subjects--Topical Terms:
205961
Data Interpretation, Statistical.
LC Class. No.: QA276.4 / .C37 2013
Dewey Class. No.: 610.72/4
National Library of Medicine Call No.: WA 950
Multiple imputation and its application /
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Multiple imputation and its application /
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James R. Carpenter and Michael G. Kenward.
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Machine generated contents note: Reasons for missing data Summary -- The Multiple Imputation Procedure and Its Justification -- Multiple imputation of binary and ordinal data -- Imputation of unordered categorical data -- Non-linear relationships -- Interactions -- Survival data, skips and large datasets -- Multilevel multiple imputation -- Sensitivity analysis: MI unleashed -- Including survey weights -- Robust Multiple Imputation.
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