If you mean using random effects which have a fat-tailed distribution
this has been available in AD Model Builder's random effects package for
some time now. The general idea is to start with a random effect assumed
to be standard normal and then to transform it by the cumulative dist
function for
, modeling the random effects with t distributions. No
software were publicly available, as far as I know.
Andy
From: S Ellison
> Sent: Thursday, March 11, 2010 9:56 AM
> To: r-help at r-project.org
> Subject: [R] Robust estimation of variance components for a
> nested design
&
bject: [R] Robust estimation of variance components for a
> nested design
>
> One of my colleagues has a data set from a two-level nested
> design from
> which we would like to estimate variance components. But we'd
> like some
> idea of what the inevitable outlier
One of my colleagues has a data set from a two-level nested design from
which we would like to estimate variance components. But we'd like some
idea of what the inevitable outliers are doing, so we were looking for
something in R that uses robust (eg Huber) treatment and returns robust
estimates of
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