Deconvolution-Based Score Tests in Measurement Error Models

Deconvolution Based Score Tests in Measurement Error Models

Consider a generalized linear model with response Y and scalar predictor X. Instead of observing X, a surrogate W = X + Z is observed where Z represents measurement error and is independent of X and Y. The efficient score test for the absence of association depends on m(w) = E(XIW = w) which is generally unknown (Tosteson and Tsiatis, 1988). Assuming that the distribution of Z is known, asympto...

Score Tests in Generalized Linear Measurement Error Models

Rank tests and regression rank score tests in measurement error models

The rank and regression rank score tests of linear hypothesis in the linear regressionmodel are modified for measurement error models. The modified tests are still distribution free. Some tests of linear subhypotheses are invariant to the nuisance parameter, others are based on the aligned ranks using the R-estimators. The asymptotic relative efficiencies of tests with respect to tests in model...

The Invariance of Score Tests to Measurement Error

The linear measurement error model is an alternative to the classical regression model, in which we assume that the independent variables are subject to error. This assumption can cause statistical inferences and parameter estimators to di er dramatically from those obtained from the classical regression model. However, inferences may remain unchanged even though the independent variables are a...

TESTING FOR AUTOCORRELATION IN UNEQUALLY REPLICATED FUNCTIONAL MEASUREMENT ERROR MODELS

In the ordinary linear models, regressing the residuals against lagged values has been suggested as an approach to test the hypothesis of zero autocorrelation among residuals. In this paper we extend these results to the both equally and unequally replicated functionally measurement error models. We consider the equally and unequally replicated cases separately, because in the first case the re...