Consistency of Regression Estimates When Some Variables are Subject to Error.
Abstract
For a general univariate 'errors-in-variables' model, the maximum likelihood estimate of the parameter vector (assuming normality of the errors), which has been described in the literature, can be expressed in an alternative form. In this form, the estimate is computationally simpler, and deeper investigation of its properties is facilitated. In particular, we demonstrate that, under conditions a good deal less restrictive than those which have been previously assumed, the estimate is weakly consistent. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 1981
- Accession Number
- ADA097008
Entities
People
- Paul P. Gallo
Organizations
- University of North Carolina at Chapel Hill