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A lack of clarity and precision in the use of estimation terms reduces the interpretability of estimation accuracy results, makes the communication of estimates difficult, and lowers the learning possibilities.
Considering the lagged dependent variable as observed or latent leads to different implications in both economic and estimation terms.
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The proposed controller consists of three terms: a time-delay-estimation term that cancels nonlinearities of the UVMS dynamics, a Terminal Sliding Mode (TSM) term that provides a fast response and a PID term that reduces the tracking error.
We compare this model to normalized reverse correlation (NRC), the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs.
Figure 6 shows that the proposed technique outperforms the data-aided channel estimation in terms of the BER.
The results show that the proposed method outperforms the least squares estimation in terms of the mean squared error.
In Fig. 15, we compare the performance of different algorithms for the distributed spectrum estimation in terms of MSD.
Meanwhile, the LP1, LP2, and LP5 have provided the best estimation in terms of value and position of critical stories.
It is indicated that robust M-estimation outperforms existing methods of estimation in terms of statistical precision and computational speed, while keeping good robustness properties.
This paper deals with cooperative position estimation in terms of pure relative localisation, which is based only on mutual observations among the robots.
The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV).
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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