4. Mixed Linear Models

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

Published Online: 1 DEC 2010

DOI: 10.1002/9780470740538.ch4

Robust Methods in Biostatistics

Robust Methods in Biostatistics

How to Cite

Heritier, S., Cantoni, E., Copt, S. and Victoria-Feser, M.-P. (2009) Mixed Linear Models, in Robust Methods in Biostatistics, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470740538.ch4

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



  • essential energy transition – dire need for change and transition to a new energy system;
  • global regulation and governance - issues of energy, development and environment interlinked, requiring approach guided by true ‘planetary ethics’;
  • global threats and the need to act from a perspective of sustainable development need;
  • Mixed linear models (MLMs);
  • MLM formulation;
  • orthodontic growth data;
  • classical estimation and inference;
  • robust estimation and bounded influence estimators;
  • robust inference - multiple hypothesis testing of main effects;
  • checking the model - skin resistance data presenting robust MM estimates


This chapter contains sections titled:

  • Introduction

  • The MLM

  • Classical Estimation and Inference

  • Robust Estimation

  • Robust Inference

  • Checking the Model

  • Further Examples

  • Discussion and Extensions