The analysis stems from James Stein theory, shows the equivalence of shrinking and regularization and is carried using multiple kernels learning for regression and SVD analysis; a mean value estimator is built, first via a rational function and secondly via a balanced neural network architecture suitable for estimating statistical quantities and gaining symmetric expectations.
For the total data set, the mean nucleotide diversity was 0.0047 (SD = 0.0034), the mean value of Watterson's estimator was 0.0067 (SD = 0.0046), and the average haplotype diversity was 0.86 (SD = 0.16).
The mean value of the fixed-effect estimator was 0.784, the standard deviation was 0.137, the range was between 0.437 and 1.212.
A distribution is produced for each estimated variable, and the mean value is then used as a robust estimator of the variable in focuses, in this case the regression parameters.
The foreground intensity was computed as the mean value of pixels, considered a better estimator as being less susceptible to distortion from outlier values [ 25], while background intensities were computed as the median value of pixels.
By simulating the limit state function for a convenient range of sampling, the mean value of I(x i ) will be an estimator for the probability of failure.
Note this is an intuitive estimator, as it just represents the mean value of g y) with respect to the available distances y.
If our estimator is optimal (fulfilling the CRLB), then the mean value of the last column should be 1.
To design a stochastic estimator for online estimation of Wiener system of second order, this paper utilizes differential mean value theorem and the results of stochastic contraction theory.
However, the C.I. of Schneider and Excoffier's [ 57] estimator of T. bernacchi expansion overlapped with that of P. borchgrevinki (although its mean value was about half that of the latter species).
