Accession Number:

ADA160190

Title:

The Limiting Distribution of Least Squares in an Errors-in-Variables Linear Regression Model

Descriptive Note:

Technical rept. Sep 1984-Aug 1985

Corporate Author:

NORTH CAROLINA UNIV AT CHAPEL HILL DEPTOF STATISTICS

Report Date:

1985-04-01

Pagination or Media Count:

28.0

Abstract:

There is a substantial literature concerning linear regression when some of the predictors independent variables are measured with error. Such models are of importance in econometrics instrumental variables models, psychometrics correction for attenuation, models of change, and in instrumental calibration studies in medicine and industry. Recent theoretical work concerning maximum likelihood estimation in such models appears in Healy 1980, Fuller 1980, and Anderson 1984, while Reilly and Patino-Leal 1981 take a Bayesian approach. In an applied context, an investigator may either overlook the measurement errors in the predictors, or choose the classical ordinary least squares OLS estimator of the parameters because of its familiarity and ease of use. Certainly, the methodology of classical least squares theory confidence intervals, multiple comparisons, tests of hypotheses, residual analysis is considerably more developed than the corresponding errors- in-variables methodology, particularly in samples of moderate size. In this paper, it is shown that under reasonable regularity conditions such linear combinations are jointly asymptotically normally distributed.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE