The results obtained in this study reveal that there is an acceptable agreement between the data estimated by LSSVM approach and the actual data of RF and NPV.
A close correspondence between the case-control and the international data estimates can be seen.
The Spearman rank correlation test showed a significant correlation between the fluid intake data estimated with the 7-day consecutive fluid record and the 24-h dietary recall (r = 0.333; p < 0.001) (Table 3).
The linearity between data estimated from direct and indirect hybridizations (e.g., through a common reference) for the same comparison was also very high.
The basic goal of the FIML method of handling missing data is to identify the population parameter values that are most likely to have produced a particular sample of data and the discrepancy between the data and the estimated parameters is quantified by this likelihood.
The estimates of cross-covariance functions between the data arrays and the estimates of auto-covariance functions of single arrays were calculated upon varying the quantization interval in the time domain.
It is important to note that the smallest 1% of N ^ e using Pcrit = 0.0000 determined from the 100th-ranked positive value was 4134, 5308, and 5846 when N was 10,000, 30,000, and 60,000, respectively, which revealed an anomaly between the simulation results and empirical data estimates of N ^ e.
Here, it is found that there is a 10 nm difference between two depth data estimated by the conventional and proposed systems for the real image.
In most regions, prevalence estimates from THIN converged with those from the GLF over the years of the study, with good agreement between the data sources, and THIN estimates falling within the confidence intervals of the GLF, from 2006 onwards.
The same thing happens in the MS-HMC context: even if both sensors images are used, there is a mismatch between the data and the EM-estimated stationary model, which leads to unsuitable estimated parameters set.
