The phrase "a model to remove" is correct and usable in written English.
It can be used in contexts where you are discussing a model or example that needs to be eliminated or discarded.
Example: "In our analysis, we identified a model to remove due to its inefficiency in predicting outcomes."
Alternatives: "a model to discard" or "a model to eliminate".
Our connectivity analyses used a FIR model to remove evoked activity, a rigorous approach that guards against misfitting of canonical basis functions to our data, or generally a systematic influence of the main response of the ROIs tested (Summerfield et al. 2006; Norman-Haignere et al. 2012).
In the following section, we introduce a system model to remove the AGC noise components.
In contrast to the somewhat arbitrary criterion used by Ogawa et al., TRANSMODIS provides a parametric model to remove this arbitrariness.
After pre-processing the data, i.e., batch data unfolding, mean-centring and scaling, the data are then filtered using an AR model to remove the auto- and cross-correlation inherent within the pre-processed batch data.
Although the original data are non-stationary, we induce stationarity on a site-by-site basis using a non-parametric model to remove the mean trend.
In Section 2, we derive a new nonconvex variational model to remove multiplicative Gamma noise under the MAP framework.
We used a second order polynomial model to remove the slow non-stationary trends of the HRV signal.
The longitudinal feature of the dataset allows us to estimate an individual fixed effect model to remove potential time-invariant confounded factors and draw more reliable conclusions on health impacts of frequent plasma donation in unhygienic conditions.
Class year was added as a covariate to the final model to remove variance attributable to year-to-year differences among classes (Glass and Hopkins, 1996; Tabachnick and Fidell, 1996).
Parameter estimates for each covariate were calculated from the weighted least squares fit of the model to the data, following prewhitening based on an AR(1) plus white noise model to remove autocorrelations in the time series (Friston et al. 2002).
