Likelihood Distributions for Estimating Functions when Both Variables are Subject to Error

When the data for estimating a function contains errors in the independent variable, the likelihood contains the unknown true values of the independent variable as nuisance parameters. These can be eliminated from the likelihood to give a new likelihood which resembles a normal distribution with variances which depend on the unknown function. The resulting least squares equations have variable weights, and must be solved by an iterative procedure.