Estimation of bias in classical linear regression slope when the proper model is functional linear regression

Abstract

We examine the effect of applying classical linear regression when measurement error is present in x and the appropriate model is functional linear regression. The bias in the classical slop emate is characterized in terms of a single parameter, τ. An unbiased estimator , is obtained assuming a prior estimate is available of $sigma;xA confidenceinterval for x is derived which is also a confidence interval for the noncentrality parameter of th noncentral F. Then the jackknife method is used to set confidencs limits for the true slope.Results are applied to comparison of assay methods in the clinical chemistry laboratory.