The phrase "a linear estimation of" is correct and usable in written English.
It can be used in contexts involving statistics, mathematics, or data analysis where a linear model is applied to estimate a value or relationship.
Example: "The researchers provided a linear estimation of the population growth based on the previous decade's data."
Alternatives: "a linear approximation of" or "a linear prediction of".
This is achieved in this study by means of a linear estimation process based on Kalman filtering.
While this solution is impractical, due to the lack of a channel estimate for the pre-filter, it represents the best performance we can expect of a linear estimation system.
In light of the complexity of our approach (specific GECI used, resolution of spike activity, how to apply avalanche measures, etc).: A) We added a subsection on the specific GECI used ("Dynamic range and linear estimation of instantaneous firing rates using the GECI YC2.60").
A notion of inverse systems is crucial for linear estimation of both Wiener and Hammerstein systems, with in verses of the nonlinear or linear parts respectively involved.
All estimates and significance levels are listed in Table 2. Based on the linear estimation of each explanatory variable from GAM, the coefficients' estimations of latitude and weekly dew point were significantly positively associated with increasing HFMD Z rate (P < 0.0001).
A linear approximation of the estimation error variance of the FD stage is established as a function of the slope of the S-curve, which represents the gain of the equivalent timing-error detector.
The intercept of this line and the 80%-threshold represents the linear estimation of the first day of return to previous activity level (treturn).
A log-linear estimation of the seven dose responses rendered a pooled RR of 1.06 (95% CI: 0.94 1.19) (Q=13.13; P=0.04; I 2 =54%) In a sensitivity analysis iteratively removing each study from the overall analysis, we detected a curvilinear association with alcohol in all subgroups.
The obtained results show that a non-linear analysis of the sample and a non-linear estimation of the mean obtained by neural networks can be profitably used to improve the accuracy of mean value estimations, especially when a small number of realizations is provided.
