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                <term xml:lang="en">functional regression models</term>
                <term xml:lang="en">functional data</term>
                <term xml:lang="en">principal components</term>
                <term xml:lang="en">missing data</term>
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                <term xml:lang="fr">données fonctionnelles</term>
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                <term xml:lang="fr">composantes principales</term>
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              <p>Time-average approximation and principal component analysis of the stochasticprocess underlying the functional data are the main tools for adapting NIPALSalgorithm to estimate missing data in the functional context. The influence of theamount of missing data in the estimation of linear regression models is studiedusing the PLS method. A simulation study illustrates our methodology.</p>
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