Multiple imputation for missing data via sequential regression trees
LF Burgette, JP Reiter - American journal of epidemiology, 2010 - academic.oup.com
Multiple imputation is particularly well suited to deal with missing data in large epidemiologic
studies, because typically these studies support a wide range of analyses by many data …
studies, because typically these studies support a wide range of analyses by many data …
[引用][C] Multiple Imputation for Missing Data via Sequential Regression Trees
LF BURGETTE, JP REITER - American journal of …, 2010 - pascal-francis.inist.fr
Multiple Imputation for Missing Data via Sequential Regression Trees CNRS Inist Pascal-Francis
CNRS Pascal and Francis Bibliographic Databases Simple search Advanced search Search by …
CNRS Pascal and Francis Bibliographic Databases Simple search Advanced search Search by …
Multiple imputation for missing data via sequential regression trees.
LF Burgette, JP Reiter - 2010 - cabidigitallibrary.org
Multiple imputation is particularly well suited to deal with missing data in large epidemiologic
studies, because typically these studies support a wide range of analyses by many data …
studies, because typically these studies support a wide range of analyses by many data …
Multiple imputation for missing data via sequential regression trees.
LF Burgette, JP Reiter - American Journal of Epidemiology, 2010 - europepmc.org
Multiple imputation is particularly well suited to deal with missing data in large epidemiologic
studies, because typically these studies support a wide range of analyses by many data …
studies, because typically these studies support a wide range of analyses by many data …
[PDF][PDF] Multiple Imputation for Missing Data via Sequential Regression Trees
LF Burgette, JP Reiter - 2010 - Citeseer
Multiple imputation is particularly well suited to deal with missing data in large epidemiologic
studies, because typically these studies support a wide range of analyses by many data …
studies, because typically these studies support a wide range of analyses by many data …
Multiple imputation for missing data via sequential regression trees
LF Burgette, JP Reiter - American journal of epidemiology, 2010 - pubmed.ncbi.nlm.nih.gov
Multiple imputation is particularly well suited to deal with missing data in large epidemiologic
studies, because typically these studies support a wide range of analyses by many data …
studies, because typically these studies support a wide range of analyses by many data …
[DOC][DOC] Multiple Imputation for Missing Data via Sequential Regression Trees
LF Burgette, JP Reiter - stat.duke.edu
Multiple imputation is particularly well suited to deal with missing data in large
epidemiological studies, since typically these studies support a wide range of analyses by …
epidemiological studies, since typically these studies support a wide range of analyses by …
Multiple Imputation for Missing Data via Sequential Regression Trees.
LF Burgette, JP Reiter - American Journal of Epidemiology, 2010 - search.ebscohost.com
Multiple imputation is particularly well suited to deal with missing data in large epidemiologic
studies, because typically these studies support a wide range of analyses by many data …
studies, because typically these studies support a wide range of analyses by many data …
[PDF][PDF] Multiple Imputation for Missing Data via Sequential Regression Trees
LF Burgette, JP Reiter - Practice, 2010 - Citeseer
Multiple imputation is particularly well suited to deal with missing data in large epidemiologic
studies, because typically these studies support a wide range of analyses by many data …
studies, because typically these studies support a wide range of analyses by many data …