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Inverse variance weighting ensures optimal parameter estimation in least-squares fitting, with exact parameter standard errors for linear least-squares with known data variance. In this Feature, I emphasize the virtues of numerical methods for estimating data variance functions and for determining these limits for any calibration model, linear or nonlinear.
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Joel Tellinghuisen (2019) studied this question.
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