On Wed, 30 Oct 2002 23:22:05 +0100
DCTS <dcts@dcts.de> wrote:
>
> I am confronted with a Logit-regression, in which y=0 is much less frequent
> than y=1. It is argued that the less frequent observations with y=0 should
> receive higher weights in the regression, such that the proportion is
> balanced between Ys being 0 and 1.
Who argues that? No, you don't want to distort the data. If your sample is a
random sample from the population to which you want to infer, then rely on
maximum likelihood to give good parameter estimates. You weight observations
if you oversampled a segment of the population and you want to represent the
original population [even then don't always weight as this reduces efficiency
when compared with covariate adjustment for oversampling factors].
Frank Harrell
>
> To my knowledge there are usually two motivations to use weights others than
> unity:
> - prior knowledge of the probability of y=0
> - optimisation of a cost function (in the example above y=0 is much more
> expensive and should be predicted with higher attention)
>
> In my limited econometric library and in the internet I wasn't able to find
> a discussion on the issue of weighting observations. If someone has a good
> hint to a source or could sketch the ideas of consequences, pros and cons I
> would be very pleased.
>
>
> Thank you,
> Thomas
>
> --------------------------------------------------------------------
> This message was distributed by s-news@lists.biostat.wustl.edu. To
> unsubscribe send e-mail to s-news-request@lists.biostat.wustl.edu with
> the BODY of the message: unsubscribe s-news
--
Frank E Harrell Jr Prof. of Biostatistics & Statistics
Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences
U. Virginia School of Medicine http://hesweb1.med.virginia.edu/biostat
|