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