Skip to main content

Semiparametric estimators of functional measurement error models with unknown error

Buy Article:

$43.00 plus tax (Refund Policy)


We consider functional measurement error models where the measurement error distribution is estimated non-parametrically. We derive a locally efficient semiparametric estimator but propose not to implement it owing to its numerical complexity. Instead, a plug-in estimator is proposed, where the measurement error distribution is estimated through non-parametric kernel methods based on multiple measurements. The root n consistency and asymptotic normality of the plug-in estimator are derived. Despite the theoretical inefficiency of the plug-in estimator, simulations demonstrate its near optimal performance. Computational advantages relative to the theoretically efficient estimator make the plug-in estimator practically appealing. Application of the estimator is illustrated by using the Framingham data example.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Errors in variables; Latent variables; Measurement error; Semiparametric efficiency; Semiparametric methods

Document Type: Research Article

Affiliations: 1: Australian National University, Canberra, Australia 2: Texas A&M University, College Station, USA

Publication date: 01 June 2007

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more