Skip to main content
SpringerLink
Log in

Missing Data Imputation in Multivariate Analysis

  • Conference paper
Data Science, Classification, and Related Methods

  • 2025 Accesses

Summary

A new imputation method for incomplete data is suggested, to be used with non-random multivariate observations. The principle is to fill the gaps in a data set so that the partial complete-data configuration and the total filled-in configuration are similar, according to a matrix correlation coefficient. The optimality criteria are Escoufier’s RV and Procrustes normalized statistic. Three examples are illustrated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Crettaz De Roten F. and Helbling, J.-M. (1991): Une estimation de données manquantes basée sur le coefficient RV. Revue de Statistique Appliquée, 39, 47–57.

    Google Scholar 

  • Everitt, B. S. (1984): An. introduction to latent variable models. Chapmann and Hall. Romanazzi, M. (1995): Missing values imputation and matrix correlation. Quaderni di Statistica e Matematica applicata alle Scienze Economico-Sociali, 15, 41–59.

    Google Scholar 

  • Seber, G. A. F. (1984): Multivariate Observations. Wiley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistics, “Ca’ Foscari” University of Venice, 2347 S. Polo, 30125, Venice, Italy

    Mario Romanazzi

Authors
  1. Mario Romanazzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106, Japan

    Chikio Hayashi , Noboru Ohsumi  & Yasumasa Baba ,  & 

  2. School of Management, Science University of Tokyo, 500 Shimokiyoku, Kuki, Saitama 346, Japan

    Keiji Yajima

  3. Institut für Statistik, Rheinisch-Westfälische Technische Hochschule (RWTH), D-52056, Aachen, Germany

    Hans-Hermann Bock

  4. Faculty of Environmental Science & Technology, Okayama University, 2-1-1 Tsushima-naka, Okayama 700, Japan

    Yutaka Tanaka

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer Japan

About this paper

Cite this paper

Romanazzi, M. (1998). Missing Data Imputation in Multivariate Analysis. In: Hayashi, C., Yajima, K., Bock, HH., Ohsumi, N., Tanaka, Y., Baba, Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65950-1_61

Download citation

  • DOI: https://doi.org/10.1007/978-4-431-65950-1_61

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-70208-5

  • Online ISBN: 978-4-431-65950-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation