Research Method. Lecture 13 (Greene Ch 16) Maximum Likelihood Estimation (MLE). Basic idea. Maximum likelihood estimation (MLE) is a method to find the most likely density function that would have generated the data. Thus, MLE requires you to make a distributional assumption first.
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(Greene Ch 16)
Maximum Likelihood Estimation (MLE)
“Which distribution, A or B, is more likely to have generated the data?”
Data value around the center of the distribution A, but not around the center of the distribution B.
Graphical illustration of the likelihood contribution
The likelihood contribution of the first observation
This notation means you multiply from i=1 through n.
Data point estimating the model: y=
The likelihood contribution of the 2nd observation
If Y=1, it means that y*≥0
If Y=0, it means that y*<0