Sentence examples for this stepwise algorithm from inspiring English sources

Although this stepwise algorithm is not guaranteed to find the global optimum interaction model, it provides at least a local optimum interaction model with some marginal effects.

This stepwise algorithm incorporated both the American College of Rheumatology (ACR) criteria for EGPA and GPA and the Chapel Hill Consensus Conference (CHCC) definition of EGPA, GPA, and MPA [ 2].

Radiological progression and first subsequent therapy were not considered in the stepwise algorithm to avoid multicollinearity with treatment discontinuation.

The stopping criterion arrested the stepwise algorithm at the thirteenth step, after eleven predictor variables were selected: two variables (O2ER and IABP) entered twice.

Although not selected by the stepwise algorithm, in the multivariate model we also included annual family income in Nepali rupees as an additional indicator of socioeconomic status, and age, because it was a matching variable.

The covariates selected by the stepwise algorithm were confirmed by testing whether their apparent associations remained when combined with the other selected covariates in a linear logistic model in which inclusion was forced.

In the multivariable modeling, treatment discontinuation (important and statistically significant predictor of HSUVs), AEs, randomized group (to reflect potential differences in HSUVs between groups), and BRCAm status (statistically significant predictor of HSUVs) were included in the stepwise algorithm.

In order to detect any thresholds, Restricted Cubic Spline (RCS) with 4 knots (i.e. 1 term decomposed into 3 terms: x, x1 and x2) [ 18] and Cox proportional hazard regression with the stepwise algorithm (p < 0.05 for entry and stay) were used to obtain a group of significant predictors of CHD.

In this paper, we present a novel stepwise algorithm for bandwidth measurement that performs accurately in xDSL service networks.

For this latter assessment, we used a forward stepwise algorithm with P-values 0.15 and 0.20 for variable entry and exit criteria for the main variables and 0.05 and 0.10 for the interaction terms (Hosmer and Lemeshow, 2000).

This leads to the improvement of the performance of regression by a stepwise algorithm with R2=0.86.