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Missing Data Project

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Compare MICE, argeimpute, and/or probably some maximum
likelihood methods for normal distribution up to 99% missing
     
Illustration of situations where prediction mean matching may
fail,and to develop a diagnostic how a researcher can detect
a priori that he/she is in such situation
     
Study the situation when there are multiple covariates and/or
outcome missing simultaneously, and check how bad it can get
     
Study categorical missing covariates using Fisher's optimum
scoring algorithm, and study how good the prediction mean
match based on canonical variable scores is comparing to
polytomous logistic full model or MICE
     
Over-fitting (or over-imputing) issue      
Compare CC, MICE, and dropping missing variable for diagnostic
data. There will be one continuous variable and two binary variables
in two settings: (a) continous variable is missing (b)one of the binary
variables is missing The missing percentage varies from 10% to 90%.
Kristel working Nov 27,2006
Compare CC, MICE, and dropping missing variable for etiology
data. There will be one continuous variable and two binary variables
in two settings: (a) continous variable is missing (b)one of the binary
variables is missing The missing percentage varies from 10% to 90%.
Kristel working Nov 27,2006

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Topic revision: r5 - 11 Dec 2006, QingxiaChen
 

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