Considering partially linear single-index errors-in-variables model which can be described as Y = n(X T a) + ZT ßo + e when the Z' s are measured with additive errors. The general estimators established in literature are biased when ignoring the measurement errors. We proposed two estimators in...

Considering partially linear single-index errors-in-variables model which can be described as Y = n(X T a) + ZT ßo + e when the Z' s are measured with additive errors. The general estimators established in literature are biased when ignoring the measurement errors. We proposed two estimators in...

In many problems one wants to model the relationship between a response Y and a covariate X. Sometimes it is difficult, expensive, or even impossible to observe X directly, but one can instead observe a substitute variable W which is easier to obtain. By far the most common model for the...

We consider the partially linear model relating a response Y to predictors (X,T) with mean function XT Ø + g(T) when the T's are measured with additive error. We derive an estimator of Ø by modification local-likelihood method. The resulting estimator of Ø is shown to be asymptotically...

We consider the partially linear model relating a response Y to predictors (X,T) with mean function XT ß + g (T) when the X's are measured with additive error. The semiparametric likelihood estimate of Severini and Staniswalis (1994) leads to biased estimates of both the parameter ß and the...