Score Tests in Generalized Linear Measurement Error Models

Score Tests in Generalized Linear Measurement Error Models

Ridge Stochastic Restricted Estimators in Semiparametric Linear Measurement Error Models

In this article we consider the stochastic restricted ridge estimation in semipara-metric linear models when the covariates are measured with additive errors. The development of penalized corrected likelihood method in such model is the basis for derivation of ridge estimates. The asymptotic normality of the resulting estimates are established. Also, necessary and sufficient condition...

Influence Measures in Ridge Linear Measurement Error Models

Usually the existence of influential observations is complicated by the presence of collinearity in linear measurement error models. However no method of influence measure available for the possible effect's that collinearity can have on the influence of an observation in such models. In this paper, a new type of ridge estimator based corrected likelihood function (REC) for linear measurement e...

Generalized Partially Linear Measurement Error Models

We consider generalized partially linear measurement error models when the linear covariate is measured with additive errors. Estimators of parameter and nonparametric function obtained by local liner regression with kernel weight, the SIMEX technique, and generalized estimating equation are proposed. The asymptotic normality of the estimate of the parameter and the asymptotics of the estimate ...

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...