The error‐in‐variables model applied to parameter estimation when the error covariance matrix is unknown

This paper presents a method for Error‐in‐Variables Model (EVM) analysis when there is no prior knowledge of the error structure, but there is replication in the data. The method is presented, with examples, for cases where the error structure varies among the data, and for cases where the error structure is constant over all the data. The minimum amount of replication needed is discussed and posterior probability density functions are developed for the parameters. The results are an extension of the work of previous authors.