Results showed that the proposed Ψ0.5 based correlation improves the predictive ability and generalization performance compared to the other corresponding theoretical and regression models.
The RSSI measurements of the test described in Section 4 at full power are converted to distances using the propagation parameters taken from the theoretical linear regression model derived from the attenuation in the IEEE802.15.4 standard when evaluated between 2.5 and 83 m (see Section 5.1).
For the genes that deviate in a manner similar to RGC32 (where the observed highest quantiles are less than the highest theoretical quantiles), linear regression (LR, hereafter) gave a higher rank (41st) while Spearman gives a lower rank (86th) in the list of genes coexpressed with NM_004426 (PHC1).
In addition to these three decoders, we tested several standard decoders from machine learning and theoretical neuroscience including linear/ridge regression, nearest neighbor regression, maximum likelihood estimators, and support vector classifiers.
To fit the observed distributions with theoretical models, both linear regression and non-linear regression routines are implemented in the software.
Results also show that spatial regression provides theoretical insight into additional, unspecified processes influencing the spatial distribution of lawns, net of the effects of the independent variables.
The reconstruction problem is thus formulated as a LAD ℓ 1 regularized problem (ℓ 1-LAD) whose theoretical properties for statistical regression are studied in [20].
Yet, these theoretical results show that regression with a dichotomous y-variable may produce good class probability estimates if (Eleft( {1 |{mathbf{x}}_{new} } right)) is well approximated with the data at hand.
This result indicated that Group 1 and Group 2 had the same structural model including similarity in the factorial structure, the theoretical construct, and structural regression paths.
A number of approaches are established to perform this task, such as setting up Bayesian Networks [ 5], information theoretical approaches [ 6- 8], regression based inference [ 9- 11], and differential equation models [ 12- 17].
