Date:         Tue, 15 Aug 2006 21:28:47 -0500
Reply-To:     Gary Rosin <grosin@stcl.edu>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Gary Rosin <grosin@stcl.edu>
Subject:      Re: Loss function for log-likelihood nonlinear regression of
              proportion via Inverse Logit
In-Reply-To:  <006a01c6c0d9$f1ed6120$a200a8c0@NOTEBOOK>
Content-Type: text/plain; charset="us-ascii"; format=flowed

At 09:16 PM 8/15/2006, Hector Maletta wrote:
>If you predict the binary exam outcome (pass or fail) via logistic
>regression, you are predicting the logit (i.e. the natural logarithm of
>[p/(1-p)]) as a linear function of the predictors. The log likelihood is
>produced as a matter of course. But for doing so you would need
>individual, not aggregated data ... .  ***

Thanks, but I want to do nonlinear regression using the model
Pass Rate (PR) =
   exp(b0 + b1*x1+ ... + bn*xn)/(1+exp(b0 + b1*x1+ ... + bn*xn))
What I need is the formula for the log-likelihood loss function, so that
I can use that instead of  least-squares.

Gary