Likelihood Distributions for Estimating Functions when Both Variables are Subject to Error |
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| Authors: | M Clutton-Brock |
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| Affiliation: | The City University , London , E.C.I. |
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| Abstract: | 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. |
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| Keywords: | Selection-of-variables problem Least Squares Linear Models C p Integrated Mean Square Error AEV Average Estimated Variance Stepwise regression |
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