Lecture: Heteroskedasticity, Weighted Least Squares, and Variance Estimation (Advanced Data Analysis from an Elementary Point of View)

February 12, 2013

(This article was originally published at Three-Toed Sloth , and syndicated at StatsBlogs.)

Weighted least squares estimates, to give more emphasis to particular data points. 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.

Reading: Notes, chapter 7
Optional reading: Faraway, section 11.3.

Advanced Data Analysis from an Elementary Point of View

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