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Exact(9)
Taking into account the results presented in Section 3 (see Figure 5), the H s estimation derived by Equation (9) should be improved to obtain a more robust estimator.
As k increases, the percentage of training samples increases, and a more robust estimator can be obtained; however, the validation sets become smaller.
Taking into account that our data fit a non-normal distribution, in these graphs the median resulted the more robust estimator of the "central value".
Retest reliability increases when a more robust estimator than the overall sum is used.
In order to enhance the retest reliability, an alternative and more robust estimator that is less sensitive to outliers/measurement errors might be needed.
The aim of the study was therefore to find a more robust estimator in order to account for outliers that occur by using accelerometry to measure physical activity.
Similar(51)
Estimates remain consistent even after more robust estimators are employed.
As mentioned before, there exist more robust estimators [2] and [10], but our purpose is to evaluate low complexity estimators.
In order to enhance the quality of data, we proposed to use more robust estimators such as quantiles or trimmed sums as a summary measure of physical activity instead of the overall sum.
Once again the proposed estimator gives more robust estimates, close to the CRLB.
Robust variance estimators were included to ensure more robust estimates of standard errors.
More suggestions(15)
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Justyna Jupowicz-Kozak
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