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During the successive regression analysis, the residual mean square is minimized using a judgement function, which, although simple, is effective in the selection of variables in the error model.
Three successive regression models were evaluated.
Successive regression models will be generated in which parameters will be either added or removed until the maximum r and minimum S are obtained.
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This is especially the case for the successive regressions and transgressions over the last interglacial cycle, which have shaped the deposition, preservation and erosion patterns of unconsolidated sediments currently submerged on continental shelves.
Variables were eliminated one at a time in successive regressions if p≥0.10, and retained if p<0.10 so as not to miss clinically important trends.
This consists in randomly perturbing the dataset many times, running LASSO on those perturbed datasets and combining the successive regressions to obtain importance scores for each feature, based on the frequency with which they are selected in the successive LASSO runs.
In this study, the thermal error modeling is based on a correlation grouping and a successive linear regression analysis.
We then used non-missing monitor values as the "independent" variable in successive linear regression models to interpolate missing values at each of the other monitors (dependent variables), starting with Bronx A because it had the fewest missing values (n = 348).
We performed successive independent regressions of the error between the constant and instantaneous RS with respect to zenith angle, day of year, and net infrared to obtain an RS function.
These four conditions, demonstrated by paths a, b, c, and c' in Figure 1, were tested through four successive linear regressions - models 1, 2, 3, and 4, respectively in Table 5.
Successive transgression and regression of the sea levels were revealed demonstrating clearly the oscillating nature of the Flandrian transgression.
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