1. Introduction

  1. Stephane Heritier1,
  2. Eva Cantoni2,
  3. Samuel Copt3 and
  4. Maria-Pia Victoria-Feser4

Published Online: 1 DEC 2010

DOI: 10.1002/9780470740538.ch1

Robust Methods in Biostatistics

Robust Methods in Biostatistics

How to Cite

Heritier, S., Cantoni, E., Copt, S. and Victoria-Feser, M.-P. (2009) Introduction, in Robust Methods in Biostatistics, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470740538.ch1

Author Information

  1. 1

    The George Institute for International Health, University of Sydney, Australia

  2. 2

    Department of Econometrics, University of Geneva, Switzerland

  3. 3

    Merck Serono International, Geneva, Switzerland

  4. 4

    HEC Section, University of Geneva, Switzerland

Publication History

  1. Published Online: 1 DEC 2010
  2. Published Print: 17 APR 2009

ISBN Information

Print ISBN: 9780470027264

Online ISBN: 9780470740538



  • robust statistics – systematic pursuit of knowledge involving recognition and formulation of a problem;
  • robust methods and outlier detection techniques;
  • modern robust statistics and pioneering works of Tukey, Huber and Hampel;
  • Against what is robust statistics robust? - robust statistics producing consistent and efficient estimators, test statistics with stable level and power;
  • diagnostic methods - alternative to robust statistics relying on ‘residual analysis’;
  • diagnostic tools based on classical estimators of model parameters;
  • classical estimators - sample means, variances and Pearson correlation;
  • robust statistics and other statistical procedures in practice;
  • robust methods based on specification of a core (parametric) model Fθ - linear regression model, mixed linear model, generalized linear model (GLM)


This chapter contains sections titled:

  • What is Robust Statistics?

  • Against What is Robust Statistics Robust?

  • Are Diagnostic Methods an Alternative to Robust Statistics?

  • How do Robust Statistics Compare with Other Statistical Procedures in Practice?