Large Sample Theory in a Semiparametric Partially Linear Errors-in-variables Models
Author | : Hua Liang |
Publisher | : |
Total Pages | : |
Release | : 1997 |
ISBN-10 | : OCLC:930878197 |
ISBN-13 | : |
Rating | : 4/5 (97 Downloads) |
Book excerpt: 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 function g(·) when measurement error is ignored. We derive a simple modification of their estimator which is a semiparametric version of the usual parametric correction for attenuation. The resulting estimator of ß is shown to be consistent and its asymptotic distribution theory is derived. Consistent standard error estimates using sandwich-type ideas are also developed. -- Measurement Error ; Errors-in-Variables ; Functional Relations ; Non-parametric Likelihood ; Orthogonal Regression ; Partially Linear Model ; Semiparametric Models ; Structural Relations