The Effects of Variance Function Estimation on Prediction and Calibration: An Example.
Abstract
This document considers fitting a straight line to data when the variances are not constant. In most fields, it is fairly common folklore thats how one estimates the variances does not matter too much when estimating the regression function. While this may be true, most problems do not stop with estimating the slope and intercept. Indeed, the ultimate goal of a study may be a prediction or a calibration. It is shown by an example that how one handles the variance function can have large effects. The point is almost trivial, but so often ignored that it is worth documenting. Additionally, this points out that one ought to spend time trying to understand the structure of the variability, a theoretical field that is not particularly well developed. Keywords: weighted least squares; heteroscedasticity.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1986
- Accession Number
- ADA174941
Entities
People
- Raymond J. Carroll
Organizations
- University of North Carolina at Chapel Hill