We used two indices of fit, namely the mean square information-weighted statistic (infit) and the outlier-sensitive statistic (outfit).
To test whether the items fit the expected model, the mean square information-weighted statistic (infit) and the outlier-sensitive statistic (outfit), were computed.
Unidimensionality was assessed with two indices of fit, namely the mean square information-weighted statistic (infit) and the outlier-sensitive statistic (outfit), with values between 0.7 and 1.3 indicating a good fit [ 25], and a principal component analysis (PCA) of the residuals.
Here we do not assume any trajectorial condition but mean square change information of (f(X t),t)) is expressed in terms of its mean square modulus of continuity.
Abbreviations: RMSE root mean square error, AIC Akaike information criterion, AICc corrected AIC, BIC Bayesian information criterion.
The Csiszar's f-divergence Γ f incorporated most of special cases of probability measure distances, including the variation distance, χ 2 -divergence, information for discrimination or generalized entropy, information gain, mutual information, mean square contingency, etc. Γ f has many applications to almost all applied sciences where stochastics enters.
From the various image quality assessment table and graphs, it has been clear that the proposed fusion technique outperforms other methods in terms of entropy, correlation coefficient, peak signal to noise ratio, root mean square error, mutal index information and edge association.
The mean square error satisfies the information inequality [9].
The root mean square roughness (RMS roughness) information of all samples is listed in Table 1.
When passive microrheology is performed in a viscoelastic medium at thermal equilibrium, i.e. with no additional energy supply, the mean square displacement analysis provides information on the rheology of the material, and yields the frequency-dependent complex shear modulus.
The ultimate choice for the model is made based on the goodness of fit criteria such as the least residual mean square error and Akaike Information Criteria.
