Use "robustness" in a sentence
robustness example sentences
robustness
1. Typical Earth design, he thought: all hard angles, to give the impression of solidity and robustness – reassurance that this vessel can withstand the most extreme conditions space can offer it
2. Architecture shall be verified for scalability, maintenance, portability, performance, robustness and open standards
3. for effectiveness, robustness, but not many
4. be used to satisfy the objective of proving the reliability and robustness of the system in service
5. the belief that the nature and robustness of the purification process is directly related to the
6. The 3rd attribute is staying power toughness which is the health and physical robustness to endure sustained bouts of stress and pressure without buckling
7. Were the same primitive treatment applied to the later work painted in the oil medium as has been used by Botticelli in his tempera picture, the robustness of the curves would have offended and been too gross for the simple formula; whereas overlaid and hidden under such a rich abundance of natural truth as it is in this gorgeous picture, we are too
8. • Besides out-of-sample analysis, other ways to mitigate overfitting bias include requiring a certain economic logic from regularities (incorporating economic priors is useful but in reality they too reflect our experience); conducting cross-validation exercises (testing findings on a dataset other than the one used for fitting the model); extensive robustness checks (subperiod consistency, etc
9. However, when the authors use the Fung–Hsieh (2004) seven-factor model, which includes alternative betas, as a robustness check, they find a higher alpha of 5
10. Some critics questioned the robustness of their findings
11. • Robustness of optimal solution—The extent of the objective function variability in the area of optimization space that surrounds the optimal solution node
12. Although the term “robustness” is widely used in statistics, economics, and biology, it has no strict mathematical formalization when applied to optimization issues
13. There is another (and not less important) criterion for selecting an optimal solution: its robustness
14. Taking the concept of robustness into account, the selection of 120 days as the optimal solution may not be the best one
15. However, we should mention that the robustness of this optimal solution is not the same for the two parameters
16. Hence, the robustness of the global maximum relative to the first parameter is higher than relative to the second one
17. If some local maximum has an objective function value that is not significantly lower than the global maximum, but, at the same time, its robustness is much higher, it may turn out that the best decision would be to select this local maximum as an optimal solution
18. However, since these areas are very small, they are inferior to the global maximum area in terms of robustness
19. In the next sections we discuss the selection of an optimal area on the basis of its relief characteristics and quantitative estimates of robustness
20. Selection of the Optimal Solution on the Basis of Robustness
21. Although robustness has no strict mathematical definition with regard to optimization procedure, we can state that an optimal solution located on the smooth surface is more robust than a solution on the broken surface
22. In order to base the selection of the optimal solution not only on the altitude mark, but on robustness as well, it is necessary to evaluate quantitatively the relief of the optimal area and the extent of its roughness
23. This method of robustness evaluation is similar to the concept of moving averages
24. For analyzing the relief of optimization space and evaluating the robustness of the optimal solution, averaging of the objective function is performed while moving through the optimization space
25. Therefore, applying this method enables us to take into account the robustness of the optimal solution
26. The averaging method described in the preceding section takes into account the height (objective function value) and smoothness (robustness) of the optimal area, but the influence of the former characteristic is greater than that of the latter one
27. The method proposed in this section assigns much greater weight to the robustness
28. As opposed to this, optimal areas of the transformed optimization space represent fairly smooth and wide plateaus of average height, which might be preferable from the robustness point of view
29. Since the new optimal areas are almost equivalent in respect to both the objective function values and the robustness, we can select one of them by the size of the surface area and its shape
30. Here we propose one possibility to solve the problem of selecting the optimal solution while taking into account its robustness
31. This assumption is quite realistic since a greater area may indicate a superior combination of two important characteristics—higher value of the objective function at the extreme point and higher robustness of the potential optimal solution
32. Therefore, in this case the advantages of the wider and more sloping surface of the right area (that is, the advantage of robustness) outweighed the advantage of the higher objective function value of the left area
33. In the preceding section we compared optimal areas of the optimization space on the basis of their robustness
34. We defined robustness as the sensitivity of the objective function to small changes in values of the parameters under optimization
35. The desired property of the optimal solution is high robustness (that is, nonsensitivity to parameter changes)
36. It is important to emphasize that in this context we talk about the robustness relative to optimized parameters
37. In this section we will discuss another aspect of robustness: the extent of optimization space sensitivity to fixed (that is, non-optimized) parameters
38. The robustness of the optimization space relative to small changes in values of the fixed parameters and to inessential alterations of the strategy structure is an important indicator of optimization reliability
39. When referring to the sensitivity of the optimization space to any changes other than changes in optimized parameters (which are measured by robustness), we will use the term “steadiness
40. Hence, the robustness of the optimal solution cannot be estimated
41. As we repeatedly outlined earlier, robustness is one of the main properties that defines optimization reliability
42. Inability to assess the robustness may challenge the validity of optimal solution
43. After that the robustness can be estimated using the methods described in section 2
44. • Special methods for testing the robustness of the backtesting system
45. Nevertheless, our experience suggests that when the strategy is reoptimized, its future robustness is higher than it would be if it had been optimized at an unchanging historical period (provided that it is tested during a sufficiently long historical interval and generates a sufficient number of position opening signals)
46. If the strategy is unstable (small changes in parameter values lead to significant deterioration of strategy performance), it is most likely overfitted (different aspects of stability, which is also called “robustness,” were discussed in Chapter 2)
47. The robustness of the strategy with respect to small changes in parameter values and time series indicates lower overfitting risk
48. 91 demonstrates, by combining several accounting variables into a single master composite, you increase the robustness of the analysis and get better, more consistent overall results
49. I was intrigued by this notion of merging value variables, even though I felt that the time period of their study—1990 through 1996—wasn’t long enough to truly test the efficacy or robustness of the strategy
50. My experience is that a stop loss will decimate the robustness of a trading strategy if it is built into the strategy and becomes an integral part of it