Model selection criteria often arise by constructing estimators of measures known as expected overall discrepancies. Such measures provide an evaluation of a candidate model by quantifying the disparity between the true model which generated the observed data and the candidate model. However, attention is seldom paid to the problem of accounting for discrepancy estimator variability, or to the ...

In optical and infrared long-baseline interferometry, data often display significant correlated errors because of uncertain multiplicative factors such as the instrumental transfer function or pixel-to-visibility matrix. context model fitting, this situation leads to a bias in parameters. most severe cases can result fit lying outside range measurement values. This is known nuclear physics Peel...