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MLR and PCR yielded remarkably different results not only concerning the bias term of ME but also concerning the variance estimates of the ME.
Generally, the bias term of ME decreased for both MLR and PCR with larger training data set sizes in the inner loop (Figure 1) since the variable selection algorithm expectedly identified on average more of the true variables with increasing training data set sizes (Additional file 1: Figure S4 shows the average percentage of truly selected variables).
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We talk more and more in terms of "me" and rarely in terms of "us".
Imagine if we stopped thinking in terms of "me" and started thinking of "we".
Thought leaders do not think in terms of "me" and "mine".
Will we be surprised if they think of society primarily in terms of "me" rather than "we" when they grow up?
The worse outcomes in terms of ME and MV with the hybrid Kalman filter might be due to its wide range of variability in the reconstructed trajectories.
In terms of me, does not like me.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com