ON THE ROBUST ANALYSIS OF VARIANCE COMPONENTS MODELS FOR PEDIGREE DATA
- 1 March 1993
- journal article
- Published by Wiley in Australian Journal of Statistics
- Vol. 35 (1) , 43-57
- https://doi.org/10.1111/j.1467-842x.1993.tb01311.x
Abstract
Summary: Quantitative traits measured over pedigrees of individuals may be analysed using maximum likelihood estimation, assuming that the trait has a multivariate normal distribution. This approach is often used in the analysis of mixed linear models. In this paper a robust version of the log likelihood for multivariate normal data is used to construct M‐estimators which are resistant to contamination by outliers. The robust estimators are found using a minimisation routine which retains the flexible parameterisations of the multivariate normal approach. Asymptotic properties of the estimators are derived, computation of the estimates and their use in outlier detection tests are discussed, and a small simulation study is conducted.Keywords
This publication has 23 references indexed in Scilit:
- Robust Estimation & TestingWiley Series in Probability and Statistics, 1990
- Review of FisherGenetic Epidemiology, 1988
- Robust inference for variance components models in families ascertained through probands: I. Conditioning on proband's phenotypeGenetic Epidemiology, 1987
- Robust Estimation of Variance ComponentsTechnometrics, 1986
- Robust tests for time series with an application to first-order autoregressive processesBiometrika, 1985
- Use of robust variance components models to analyse triglyceride data in familiesAnnals of Human Genetics, 1985
- Extensions to multivariate normal models for pedigree analysisAnnals of Human Genetics, 1982
- Robust StatisticsPublished by Wiley ,1981
- On the asymptotic distribution of multivariate M-estimatesJournal of Multivariate Analysis, 1978
- The Multivariate t-Distribution Associated with a Set of Normal Sample DeviatesAustralian Journal of Physics, 1954