The phrase "a more robust estimation" is correct and usable in written English.
It can be used when discussing the need for a stronger or more reliable estimate in various contexts, such as statistics, project planning, or financial forecasting.
Example: "After reviewing the initial data, we decided to conduct further analysis to provide a more robust estimation of the project's costs."
Alternatives: "a stronger estimate" or "a more reliable assessment."
The advantage of a Bayesian approach is the ability to incorporate prior knowledge from other phenology and climate observations to reduce variability in the estimates, as well as a more robust estimation of uncertainty.
This cyclical information transfer allows a parameter estimate to "borrow strength" from the other estimates of the same parameter, leading to a more robust estimation (Finkenstadt et al. 2013).
Using estimated pitch values f ̂ 0, the spectral comb filter is build to suppress the influence of the already estimated speech sources in the CPSD; this leads to a more robust estimation of the remaining speech sources.
A major methodological contribution of the paper is the inclusion of comprehensive private vehicle costs and public transportation expenses in the model that contributes to a more robust estimation and understanding of location affordability.
Thus, a more robust estimation may significantly improve the performance.
To have a more robust estimation, a stochastic averaging process is introduced.
Though both AUC24h/MIC and Cmax/MIC were PK/PD indices of fluoroquinolones, AUC24h is a much more robust estimation than the one of a single snapshot Cmax which depends on many factors such as sampling schedule.
Refinement of modeling will lead to a unified, more robust estimation of background threshold.
Developing criteria for the objective assessment of the quality of glottal waveform estimates would facilitate the design of more robust estimation algorithms.
In particular, regional frequency analysis yields narrower uncertainty bounds and hence more robust estimation of extreme quantiles, corresponding to long return periods.
For POD repeatability represented as the standard deviations of post-fit residuals and 3D errors by repetitive PODs, however, GRS yields 25%and13%3% more robust estimation results than MRRS for post-fit residual and 3D error, respectively.
