Fitting a function to time-dependent ensemble averaged data.

ABSTRACT: Time-dependent ensemble averages, i.e., trajectory-based averages of some observable, are of importance in many fields of science. A crucial objective when interpreting such data is to fit these averages (for instance, squared displacements) with a function and extract parameters (such as diffusion constants). A commonly overlooked challenge in such function fitting procedures is that fluctuations around mean values, by construction, exhibit temporal correlations. We show that the only available general purpose function fitting methods, correlated chi-square method and the weighted least squares method (which neglects correlation), fail at either robust parameter estimation or accurate error estimation. We remedy this by deriving a new closed-form error estimation formula for weighted leas

SUBMITTER: Fogelmark K 

PROVIDER: S-EPMC5934400 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature