Multivariate linear regression with missing values

Title: Multivariate linear regression with missing values
Creator: Beyad, Yaser; Maeder, Marcel
Relation: Analytica Chimica Acta Vol. 796, p. 38-41
Publisher Link: http://dx.doi.org/10.1016/j.aca.2013.08.027
Publisher: Elsevier BV
Resource Type: journal article
Date: 2013
Description: This contribution presents and discusses an efficient algorithm for multivariate linear regression analysis of data sets with missing values. The algorithm is based on the insight that multivariate linear regression can be formulated as a set of individual univariate linear regressions. All available information is used and the calculations are explicit. The only restriction is that the independent variable matrix has to be non-singular. There is no need for imputation of interpolated or otherwise guessed values which require subsequent iterative refinement.
Subject: linear regression; missing values
Identifier: http://hdl.handle.net/1959.13/1298404
Identifier: uon:19661
Identifier: ISSN:0003-2670
Language: eng
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