Consistency of regression estimates when some variables are subject to error

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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, w demonstrate that, under conditions a good deal less restrictive than those which have been previously assumed, the estimate is weakly consistent.