Nearest neighbour in least squares data imputation algorithms for marketing data

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Marketing research operates with multivariate data for solving such problems as market segmentation, estimating purchasing power of a market sector, modeling attrition. In many cases, the data collected or supplied for these purposes may have a number of missing entries.The paper is devoted to an empirical evaluation of method for imputation of missing data in the so-called nearest neighbour of least-squares approximation approach, a non-parametric computationally efficient multidimensional technique. We make contributions to each of the two components of the experiment setting: (a) An empirical evaluation of the nearest neighbour in least-squares data imputation algorithm for marketing research (b) experimental comparisons with expectation–maximization (EM) algorithm and multiple imputation (MI) using real marketing data sets. Specifically, we review “global” methods for least-squares data imputation and propose extensions to them based on the nearest neighbours (NN) approach. It appears that NN in the least-squares data imputation algorithm almost always outperforms EM algorithm and is comparable to the multiple imputation approach.

Original languageEnglish
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer International Publishing
Pages313-330
Number of pages18
DOIs
Publication statusPublished - 1 Jan 2014

Publication series

NameSpringer Optimization and Its Applications
Volume92
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

Keywords

  • Least squares
  • Marketing data
  • Missing data
  • Nearest neighbours
  • Singular value decomposition

Fingerprint Dive into the research topics of 'Nearest neighbour in least squares data imputation algorithms for marketing data'. Together they form a unique fingerprint.

Cite this