"Alessandro De Sanctis" wrote in message <naf92r$if5$1@newscl01ah.mathworks.com>...
> Hello,
>
> I have to maximize a likelihood in which to every observation correspond a specific (non-integer) weight. In particular, I am referring to sampling weights, which denote the inverse of the probability that the observation is included in the sample.
>
> I tried by expanding the dataset (so that an observation with weight = 100 is repeated 100 times) but the dataset became extremely large and it's the second week that fminsearch is running.
>
> My ultimate goal would be to estimate a non-linear model with a binary dependent variable and weights to observations.
>
> Please any alternative idea on how to proceed is welcome. Thank you in advance.
> Alessandro
To help us help you, can you show us a small snippet of your code? The above description is pretty vague and doesn't give us much to go on.
> Hello,
>
> I have to maximize a likelihood in which to every observation correspond a specific (non-integer) weight. In particular, I am referring to sampling weights, which denote the inverse of the probability that the observation is included in the sample.
>
> I tried by expanding the dataset (so that an observation with weight = 100 is repeated 100 times) but the dataset became extremely large and it's the second week that fminsearch is running.
>
> My ultimate goal would be to estimate a non-linear model with a binary dependent variable and weights to observations.
>
> Please any alternative idea on how to proceed is welcome. Thank you in advance.
> Alessandro
To help us help you, can you show us a small snippet of your code? The above description is pretty vague and doesn't give us much to go on.
