@CONFERENCE\{IMM2011-05994,
    author       = "S. G. Karadogan and L. Marchegiani and L. K. Hansen and J. Larsen",
    title        = "How Efficient Is Estimation with Missing Data?",
    year         = "2011",
    month        = "may",
    pages        = "2260-2263",
    booktitle    = "International Conference on Acoustics, Speech and Signal Processing",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "{IEEE} Press",
    organization = "",
    address      = "",
    url          = "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5946932&openedRefinements%3D*%26filter%3DAND%28NOT%284283010803%29%29%26searchField%3DSearch+All%26queryText%3DJ.+Larsen+2011",
    abstract     = "In this paper, we represent a new evaluation approach for missing
data techniques (MDTs) where the efficiency of those are investigated
using listwise deletion method as reference. We experiment
on classification problems and calculate misclassification rates (MR)
for different missing data percentages (MDP). We compare three
MDTs: pairwise deletion (PW), mean imputation (MI) and a maximum
likelihood method that we call complete expectation maximization
(CEM). We use synthetic dataset, Iris dataset and Pima Indians
Diabetes dataset. We train a Gaussian mixture model (GMM)
with missing at random (MAR) data. We test the trained {GMM} for
two cases, in which test dataset is missing or complete. The results
show that {CEM} is the most efficient method in both cases while {MI} 
is the worst of the three. {PW} and {CEM} prove to be more stable with
respect to especially higher {MDP} values than {MI}.",
    isbn_issn    = "{DOI} 10.1109/ICASSP.2011.5946932"
}