6533b7d3fe1ef96bd125fea6

RESEARCH PRODUCT

Missing Data

Alberto FerrerFrancisco Arteaga

subject

Computer scienceIterative methodSimulated dataPrincipal component analysisExpectation–maximization algorithmPartial least squares regressionMultivariate calibrationMissing data problemData miningcomputer.software_genreMissing datacomputer

description

In this chapter, we deal with the problem of missing data in principal component analysis (PCA) and partial least squares (PLS) methods. First, we review several statistical methods proposed in the literature for handling missing data. Both single and multiple imputation (MI) methods are studied and compared using simulated data. After this, we particularize the missing data problem for building and exploiting multivariate calibration models. Several approaches proposed in the literature are introduced and their performance compared based on several real data sets.

yearjournalcountryeditionlanguage
2009-01-01
https://doi.org/10.1016/b978-044452701-1.00125-3