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Linear Model Selection When Covariates Contain Errors
Prediction precision is arguably the most relevant criterion of a model in practice and is often a sought after property. A common difficulty with covariates measured with errors is the impossibility of performing prediction evaluation on the data even if a model is completely given without any unknown parameters. We bypass this inherent difficulty by using special properties on moment relations in linear regression models with measurement errors. The end product is a model selection proceduredoi:10.6084/m9.figshare.3777696.v2 fatcat:2yjgotdvjbeozox2vmvs67uwsu