The phrase "average relative bias" is correct and usable in written English.
It can be used in contexts involving statistics, data analysis, or research to describe the mean deviation of a measurement from a true value relative to that true value.
Example: "The study found that the average relative bias of the measurements was within acceptable limits, indicating reliable data collection."
Alternatives: "mean relative error" or "typical relative bias".
Average ASR with bootstrapped 95% confidence intervals (CI) (500 replicates), the proportion of simulations in which 95% CI included the expected ASR (coverage probability), the proportion of simulations in which 95% CI did not include 1.00 (power) and average relative bias (difference between the estimated sequence ratio and the expected sequence ratio) were calculated.
In Additional file 5: Figure S5 and Additional file 6: Figure S6 we present the average relative bias for IF > 0. Relative bias decreases with the total number of individuals included in the network, the total number of trials, and the assumed IF value.
Generally, the average relative biases of parameter estimations were small (<5% of the known parameter value, with RMSE close to SE, which is a property of unbiased estimators) (Table 3).
The mean relative bias (MRB), 1 n ∑ i = 1 n e i − v i v i, was estimated, where e i is the i'th estimate of the pseudo-generated true value v, and averaged over the n = 500 data sets.
The coefficient of variation (CV) of most surface variables was found to lie between 60% and 100%, and their relative bias (average observational error divided by the mean area) between 2% and 10%.
The designs were assessed by computing bias, relative bias, mean square error and coverages of confidence intervals.
In addition, the SVR-RFFA model also performed well based on other goodness-of-fit statistics such as BIAS (mean bias) and BIASr (relative BIAS).
We have found that using bootstrapping with 500 replications to obtain the standard error reduces the bias greatly; we observed a relative bias of –2%% (on average) with the bootstrap estimator when D=3.2 compared to -20 % using the default method [ 23].
The relative bias was calculated as the average of the 1,000 estimated RR in log scale minus the log of the true RR divided by the log of the true RR.
Relative bias was defined as the average of the 1,000 estimated RR in log scale minus the log of the true RR divided by the log of the true RR.
All models perform relatively well in terms of relative bias and variation of relative bias.
