I have a question about if it is possible to fit weighted logistic
regression model using glm() in the sense of maximizing the
weighted loglikelihood, i.e., if the original log likelihood is
l=sum(l(i)) where l(i) is the contribution of the i-th individual,
can we use glm() to maixmize lw=sum(l(i)*w(i)) where w=[w(1),w(2),
...w(n)] is the vector of the specified weights.(n is the sample size)?
In glm(), there is an option weights=. Is this relevant to the above
question ?
Thanks
Ming Ji
Divison of Statistics
University of California
Davis, CA 95616
Email: mji@wald.ucdavis.edu
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