A new estimation method for Michaelis-Menten models handles varying variance and clustered data through root-finding and plug-in updates, improving inference in simulations and real enzyme data.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Variance-Aware Estimation and Inference for Michaelis--Menten Models with Heteroscedastic Errors and Clustered Measurements
A new estimation method for Michaelis-Menten models handles varying variance and clustered data through root-finding and plug-in updates, improving inference in simulations and real enzyme data.