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BMCBI
2010
110views more  BMCBI 2010»
13 years 7 months ago
Missing value imputation for epistatic MAPs
Background: Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The dat...
Colm Ryan, Derek Greene, Gerard Cagney, Padraig Cu...
HIS
2004
13 years 9 months ago
K-Ranked Covariance Based Missing Values Estimation for Microarray Data Classification
Microarray data often contains multiple missing genetic expression values that degrade the performance of statistical and machine learning algorithms. This paper presents a K rank...
Muhammad Shoaib B. Sehgal, Iqbal Gondal, Laurence ...
ICASSP
2011
IEEE
12 years 11 months ago
Evaluating music sequence models through missing data
Building models of the structure in musical signals raises the question of how to evaluate and compare different modeling approaches. One possibility is to use the model to impute...
Thierry Bertin-Mahieux, Graham Grindlay, Ron J. We...
CSDA
2007
100views more  CSDA 2007»
13 years 7 months ago
Convergence of random k-nearest-neighbour imputation
Random k-nearest-neighbour (RKNN) imputation is an established algorithm for filling in missing values in data sets. Assume that data are missing in a random way, so that missing...
Fredrik A. Dahl
CSDA
2007
110views more  CSDA 2007»
13 years 7 months ago
Two-way imputation: A Bayesian method for estimating missing scores in tests and questionnaires, and an accurate approximation
Previous research has shown that method two-way with error for multiple imputation in test and questionnaire data produces small bias in statistical analyses. This method is based...
Joost R. Van Ginkel, L. Andries Van der Ark, Klaas...