Robust estimation for epidemic models

Ian C. Marschner*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A robust approach to the analysis of epidemic data is suggested. This method is based on a natural extension of M‐estimation for i.i.d. observations where the distribution may be asymmetric. It is discussed initially in the context of a general discrete time stochastic process before being applied to previously studied epidemic models. In particular we consider a class of chain binomial models and models based on time dependent branching processes. Robustness and efficiency properties are studied through simulation and some previously analysed data sets are considered.

Original languageEnglish
Pages (from-to)221-240
Number of pages20
JournalAustralian Journal of Statistics
Volume33
Issue number2
DOIs
Publication statusPublished - 1991
Externally publishedYes

Keywords

  • Chain binomial model
  • M‐estimation
  • power series distribution
  • robustness
  • time dependent branching process

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