Simon Fraser University

Regression with Autocorrelated Errors

Ordinary regression analysis is based on several statistical assumptions. One key assumption is that the errors are independent of each other. However, with time series data, the ordinary regression ...
JSTOR Daily

Two Pitfalls of Using Standard Regression Diagnostics when Both X and Y Have Measurement Error

Modern exploratory data analysis produces models that are not based on physical theory but that are consistent with pictures of the data. When both X and Y have error ...
Risk

Computing a standard error for the Gini coefficient: an application to credit risk model validation

Resampling approaches were the first techniques employed to compute a variance for the Gini coefficient. Various authors have demonstrated that estimates of the Gini coefficient can be obtained from a ...
JSTOR Daily

Linear Regression with Non-Constant, Unknown Error Variances: Sampling Experiments with Least Squares, Weighted Least Squares and Maximum Likelihood Estimators

John A. Jacquez, Frances J. Mather and Charles R. Crawford The theory of simple linear regression is extended to the case of non-uniform error variances for the ...