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Re: st: importance weights


From   "Anders Alexandersson <aalex@its.msstate.edu>" <aalex@its.msstate.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: importance weights
Date   Wed, 19 Feb 2003 14:53:41 -0000

Importance weights do whatever you want them to do. For example, 


specify your "weight" variable to be more than 1, say 10, so that the 
iweight is less than 1. Then, in the auto dataset -reg price mpg 
[iw=1/10]- will give you 7 observations only. (I had a typo in my 
previous message, and the text got mangled) Thus, it can do the 
opposite of what you claim. 




See,


. help weights




I recommend you to stay away from importance weights in most cases, 


but they are very handy in the rare cases when you as a programmer 


need them. For example, in my confidence ellipse program (. findit 


ellip) I implemented Bartels' "fractional pooling of disparate 


observations" in the pool(#) option using fractional importance 


weights to downweigh "problematic" observations, e.g. problematic in a 


Bayesian sense.




Why do you want to use importance weights? 




Anders Alexandersson






"B. Burcin Yurtoglu" <burcin.yurtoglu@u...> wrote:


> Dear All,


> 


> The use of "importance" weights seems to increase the number of 
observations in 


> the regress command.


> 


> E.g.


> 


> reg y x


> 


> produces an output say with 8 observations


> 


> whereas 


> 


> reg y x [iw=1/weight]


> produces an output with 17 observations


> 


> Does anybody have an explanation or suggestions for reading?


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