### Moving Beyond Conditional Expectations: Weighted Least Squares, Heteroskedasticity, Variance Functions (Advanced Data Analysis from an Elementary Point of View, Lecture 5)

Average predictive comparisons. Weighted least squares
estimates. Heteroskedasticity and the problems it causes for inference. How
weighted least squares gets around the problems of heteroskedasticity, if we
know the variance function. Estimating the variance function from regression
residuals. An iterative method for estimating the regression function and the
variance function together. Locally constant and locally linear modeling.
Lowess.

*Comment*: Predictive comparisons were really a held-over topic from
the previous lecture, and I am not quite happy with putting local polynomials
here.

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Advanced Data Analysis from an Elementary Point of View

Posted at February 04, 2011 01:34 | permanent link