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Supporting Selection of Statistical Techniques

  • Kay F. Hildebrand
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In this paper we describe the necessity for a semi-structured approach towards the selection of techniques in quantitative research. Deciding for a set of suitable techniques to work with a given dataset is a non-trivial and time-consuming task. Thus, structured support for choosing adequate data analysis techniques is required. We present a structural framework for organizing techniques and a description template to uniformly characterize techniques. We show that the former will provide an overview on all available techniques on different levels of abstraction, while the latter offers a way to assess a single method as well as compare it to others.

References

  1. Dekking, F. M., Kraaikamp, C., Lopuhaa, H. P., & Meester, L. E. (2010). Modern introduction to probability and statistics. London: Springer.Google Scholar
  2. Fahrmeir, L., Künstler, R., Pigeot, I., & Tutz, G. (2007). Statistik: der Weg zur Datenanalyse; mit 25 Tabellen New York.Google Scholar
  3. Feelders, A., Daniels, H., & Holsheimer, M. (2000). Methodological and practical aspects of data mining. Information & Management, 37, 271–281.CrossRefGoogle Scholar
  4. Feller, W. (1971). An introduction to probability theory and its applications (2nd ed.) (Vol. 2, p. 669). New York: Wiley.Google Scholar
  5. Feller, W. (1968). An introduction to probability theory and its applications (3rd ed.) (Vol. 1, p. 509). New York: Wiley.Google Scholar
  6. Georgii, H.-O. (2007). Stochastik. (3rd ed.) (p. 378). Gruyter.Google Scholar
  7. Grob, H. L., & Bensberg, F. (2009). Das data-mining-konzept. Computergestütztes Controlling. Münster: Institut für Wirtschaftsinformatik.Google Scholar
  8. Härdle, W., & Simar, L. (2011). Applied multivariate statistical analysis. Berlin: Springer.Google Scholar
  9. Han, J., Kamber, M., & Pei, J. (2011). Data mining: concepts and techniques. (3rd ed.) (p. 744). Los Altos: Morgan Kaufmann.Google Scholar
  10. Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of data mining.Google Scholar
  11. Handl, A. (2002). Multivariate analysemethoden (p. 464). Berlin: Springer.Google Scholar
  12. Hartung, J., Elpelt, B., & Klösener, K.-H. (2009). Statistik. Lehr- und Handbuch der angewandten Statistik. Biometrical Journal, 15, 1145.Google Scholar
  13. Hastie, T., Tibshirani, R., & Friedman, J. (2008). The elements of statistical learning: Data mining, inference and prediction. International Statistical Review (2nd ed.) (Vol. 77, p. 745). Berlin: Springer.Google Scholar
  14. Jackson, J. (2002). Data mining: A conceptual overview. Communications of the Association for Information Systems, 8, 267–296.Google Scholar
  15. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques. Annals of Physics (Vol. 54, p. 664). Los Altos: Morgan Kaufmann.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.European Research Center for Information System (ERCIS)MünsterGermany

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