Exact(60)
Stepwise procedures have been found to produce poor variable screens [81].
Two stepwise procedures based on variance estimators are proposed for selecting the significant variables in a general nonparametric regression model.
There are several automated feature selection methods, ranging from backward, forward or stepwise procedures, to further elaborated methodologies such as evolutionary programming.
The stepwise procedures of the VIA algorithm are described as follows: Step (1): Set v0(s) = 0 for each state s.
In fact, human children over-imitate: they copy complex stepwise procedures even when simpler ways of obtaining goals are conspicuously available (Horner and Whiten 2005).
All variables associated with either falls or injuries with p values of <0.1 in univariable analyses were offered to multivariable logistic regression models developed manually using forward stepwise procedures in Stata [13].
The stepwise procedures are as follows: (i) defining the 'system boundary' of the product life cycles, (ii) data collection, (iii) compilation and validation of emissions flows and (iv) identification of hot-spots and emissions reduction opportunities.
For that purpose several search algorithms have been devised, ranging from simple heuristic approaches [26, 27] which perform a "greedy" search of the best subsets of variables such as forward selection, backward elimination or stepwise procedures to further elaborate methodologies including simulated annealing [28] and evolutionary programming [29] such as genetic algorithms [30].
Due to the high number of baseline covariates collected in the study, only variables different at baseline at a 10% significance level and variables thought to be influential (country effect, treatment as mono- versus polytherapy) were entered into the initial joint model for compliance and were then reduced using stepwise procedures.
Stepwise procedures were not used.
Backward stepwise procedures were used.
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