A variance-aware estimation and inference method for Michaelis-Menten models is proposed that uses conditionally Gaussian working models to handle heteroscedasticity and clustering.
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Variance-Aware Estimation and Inference for Michaelis--Menten Models with Heteroscedastic Errors and Clustered Measurements
A variance-aware estimation and inference method for Michaelis-Menten models is proposed that uses conditionally Gaussian working models to handle heteroscedasticity and clustering.