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
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
