A jackknife method for estimation of variance components

Christian Lavergne (1)

1 LMC - IMAG - Laboratoire de Modélisation et Calcul

This paper concerns a method of estimation of variance components in a random effect linear model. It is mainly a resampling method and relies on the Jackknife principle. The derived estimators are presented as least squares estimators in an appropriate linear model, and one of them appears as a MINQUE (Minimum Norm Quadratic Unbiased Estimation) estimator. Our resampling method is illustrated by an example given by C. R. Rao [7] and some optimal properties of our estimator are derived for this example. In the last part, this method is used to derive an estimation of variance components in a random effect linear model when one of the components is assumed to be known.

Mots clés: random effect linear models; Jackknife estimators; MINQUE

Domaines: Statistiques [math.ST]

Christian Lavergne. A jackknife method for estimation of variance components. Statistics, 1995, 27 (1-2), pp.1-13. ⟨10.1080/02331889508802506⟩. ⟨hal-00196098⟩