Bootstrap for Imputed Data in Panels: a Numerical Evaluation
by Lucia P. Barroso, Delhi T. P. Salinas
A common problem in sampling surveys is missing data. One way to
handle this problem is using imputation techniques, which consists
of predicting the missing observations to complete the data set
which will be later analyzed. Imputation techniques can also be
applied when the bootstrap method is used to estimate the standard
error of some estimator. Although such techniques have been widely
used, they have not received much attention for data sets with
missing values. The main purpose of this paper is the application
of non-parametric bootstrap techniques for data sets with missing
values using imputation techniques to replace the missing values.
We present the results of a simulation study about which deals
with the estimation of the variance of the variance-components
estimators in a mixed linear model. The samples we consider are
panels, where $n$ individuals are observed in two occasions.
BLUP, bootstrap, EM algorithm, imputation, mixed linear
model, principal component analysis
Lucia P. Barroso,
Delhi T. P. Salinas,
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