[EMAIL PROTECTED] writes:
> (a) case weights: w_i = 3 means `I have three observations like (y, x)'
>
> (b) inverse-variance weights, most often an indication that w_i = 1/3
> means that y_i is actually the average of 3 observations at x_i.
>
> (c) multiple imputation, where a case with missing values in x is split
> into say 5 parts, with case weights less than and summing to one.
>
> (d) Heteroscedasticity, where the model is rather
>
> y = x\beta + \epsilon, \epsilon \sim N(0, \sigma^2(x))
>
> And there may well be other scenarios, but those are the most common (in
> decreasing order) in my experience.
I'd have (d) higher on the list, but never mind. There's also
(e) Inverse probability weights: Knowing that part of the population
is undersampled and wanting results that are compatible with what you
would have gotten in a balanced sample. Prototypically: You sample X,
taking only a third of those with X > c; find population mean of X,
(or univariate regression on some other variable, which is only
recorded in the subsample).
(R-bugs stripped from recipients since this doesn't really have
anything to do with the purported bug.)
--
O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
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