Regression Calibration with Heteroscedastic Error Variance

Regression calibration with heteroscedastic error variance.

The problem of covariate measurement error with heteroscedastic measurement error variance is considered. Standard regression calibration assumes that the measurement error has a homoscedastic measurement error variance. An estimator is proposed to correct regression coefficients for covariate measurement error with heteroscedastic variance. Point and interval estimates are derived. Validation ...

Calibration and Regression with Nonconstant Error Variance

Ordinary least squares regression analysis is generally inappropriate for calibration and regression problems when the usual assumption ofconstant variance across all observations doesn't hold. Estimators of regression parameters are of relatively poor quality and the resulting inference can be misleading. The use of standard data transformations is a common alternative but may not provide enou...

Variance Function Estimation in Heteroscedastic Regression Models

Regression analysis with covariates that have heteroscedastic measurement error.

We consider the estimation of the regression of an outcome Y on a covariate X, where X is unobserved, but a variable W that measures X with error is observed. A calibration sample that measures pairs of values of X and W is also available; we consider calibration samples where Y is measured (internal calibration) and not measured (external calibration). One common approach for measurement error...

Assessing the adequacy of variance function in heteroscedastic regression models.

Heteroscedastic data arise in many applications. In heteroscedastic regression analysis, the variance is often modeled as a parametric function of the covariates or the regression mean. We propose a kernel-smoothing type nonparametric test for checking the adequacy of a given parametric variance structure. The test does not need to specify a parametric distribution for the random errors. It is ...