Title: A 0-1 quadratic programme for the case of missing data in regression

Authors: Brian K. Smith; Justin R. Chimka; Heather Nachtmann

Addresses: Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA ' Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA ' Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA

Abstract: Multivariate statistical analysis techniques including regression analysis compose a popular toolset for analysing survey data, but the techniques require a complete dataset with no missing values. Unfortunately, most survey datasets contain missing values. These missing values must be resolved in some manner before regression analysis can take place. We present a quadratic programming methodology for eliminating non-responses from a survey dataset.

Keywords: missing data values; quadratic programming; QP; regression analysis; survey research; non-responses.

DOI: 10.1504/IJDATS.2014.059016

International Journal of Data Analysis Techniques and Strategies, 2014 Vol.6 No.1, pp.94 - 104

Published online: 05 Jul 2014 *

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