STAT 541: More Logistic Regression

Statistics 541: Logistic Regression 3


General modeling concepts

Suppose one believes Y is a monotone function of X. Use trimmed X's to fix this problem. So regress on both X and an X truncated at say the 95% point of the data.

Computing standard errors via likelihood methods

An advantage of estimators that are linear combinations of Y's is that we can figure out SE's via a central limit theorem. This was the approach in least squares regression. (Beta-hat = (X'X)-1X'Y = wY for some weight w.)

We have two approaches. We can simply use the weights given by the last round of the IRLS, or we can use a likelihood based method.

Likelihood method for standard regression:

Likelihood method for logistic regression:

Chi-square tests

What if Y is discrete and X is discrete also?

Last modified: Tue Mar 27 08:57:19 2001