Hi,
I have a bunch of data which is assumed to be instances of a geometric random
variable with outliers. How can I do a robust estimation of the parameter p so
that the effect of outliers is minimized?
As a part of the estimation process, I also need to know which are the outliers
in the data. I found glmrob which does robust estimation of Poisson and
binomial random variables but not geometric random variable.
I understand that the maximum likelihood estimate of p of geometric random
variable is the mean of the instance values. So if we do robust estimate of
mean of the instance values, can we say that we are doing robust estimation of
the underlying geometric random variable? If so, which method is most suitable
for doing the robust mean estimation. (I am a newbie in statistics and R).
Thanks
suresh
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
