Skip to main content
SpringerLink
Log in

Some results on classifier selection with missing covariates

  • Published:
Metrika Aims and scope Submit manuscript

Abstract

Methods are proposed to choose classifiers from a given collection of classifiers when there are missing covariates in the data. Two situations are considered: (i) the case where the new observation (to be classified) has no missing covariates and (ii) the case where the new observation is also allowed to have missing covariates. Using arguments from the empirical process theory, exponential performance bounds will be derived for the resulting classifiers. Such bounds, together with the Borel-Cantelli lemma, yield various strong consistency results.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bennett G (1962) Probability inequalities for the sum of independent random variables. J Am Stat Assoc 57: 33–45

    MATH  Google Scholar 

  • Chenouri S, Mojirsheibani M, Montazeri Z (2009) Empirical Measures for incomplete data with applications. Electron J Stat 3: 1021–1038

    Article  MathSciNet  Google Scholar 

  • Devroye L, Györfi L, Lugosi G (1996) A probabilistic theory of pattern recognition. Springer, New York

    MATH  Google Scholar 

  • Dudley R (1978) Central limit theorems for empirical measures. Ann Prob 6: 899–929

    Article  MathSciNet  MATH  Google Scholar 

  • Györfi L, Kohler M, Krzyzak A, Walk H (2002) A distribution-free theory of nonparametric regression. Springer, New York

    Book  MATH  Google Scholar 

  • Little RJA, Rubin DB (2002) Statistical analysis with missing data. Wiley, New York

    MATH  Google Scholar 

  • Mojirsheibani M, Montazeri Z (2007) Statistical classification with missing covariates. J R Stat Soc Ser B 69: 839–857

    Article  MathSciNet  Google Scholar 

  • Pollard D (1984) Convergence of stochastic processes. Springer, New York

    Book  MATH  Google Scholar 

  • Van der Vaart AW, Wellner JA (1996) Weak convergence and empirical processes with application to statistics. Springer, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Carleton University, Ottawa, Ontario, K1S 5B6, Canada

    Majid Mojirsheibani

Authors
  1. Majid Mojirsheibani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Majid Mojirsheibani.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mojirsheibani, M. Some results on classifier selection with missing covariates. Metrika 75, 521–539 (2012). https://doi.org/10.1007/s00184-010-0340-6

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00184-010-0340-6

Keywords

Search

Navigation