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Estimating Individual Mahalanobis Distance in High-Dimensional Data

Author

Listed:
  • Dai, Deliang

    (Linnaeus university)

  • Holgersson, Thomas

    (Linnaeus university, Jönköping university, & Centre of Excellence for Science and Innovation Studies (CESIS))

  • Karlsson, Peter

    (Linnaeus university & Jönköping university,)

Abstract

This paper treats the problem of estimating individual Mahalanobis distances (MD) in cases when the dimension of the variable p is proportional to the sample size n. Asymptotic expected values are derived under the assumption p/n->c, 0

Suggested Citation

  • Dai, Deliang & Holgersson, Thomas & Karlsson, Peter, 2014. "Estimating Individual Mahalanobis Distance in High-Dimensional Data," Working Paper Series in Economics and Institutions of Innovation 362, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    • Handle: RePEc:hhs:cesisp:0362
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    More about this item

    Keywords

    Increasing dimension data; Mahalanobis distance; Inverse covariance matrix; Smoothing;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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