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If one considers selection based on penalized error measures, the level of penalty required to exclude the covariate is indicative of the explanatory power.
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The alternative algorithm based on penalized likelihood revealed highly congruent results with those obtained by the Bayesian approach (Table 1).
Model selection was based on penalized estimation of a linear model by using the lasso (l1) penalty [ 16, 17].
Recently, many statistical methods based on penalized regressions have been proposed to tackle the so-called "large P and small N" problem.
Estimation methods for DLNMs not requiring the completely parametric approach proposed here seems attractive and possible, in particular based on penalized likelihood [ 24] or Bayesian methods [ 25].
IsoLasso is based on penalized likelihood maximization like FlipFlop and NSMAP, but starts from a restricted set of isoforms the same set returned by Cufflinks for single-end data.
We fit general additive models based on penalized splines using both ln-transformed and untransformed annual average black carbon concentrations to assess the functional form of the outcome exposure association.
In this section, a bound on the generalization error based on the LOO Error is derived.
The error model is based on mappings (error curves) that assign error rates to specific base positions.
In contrast, the weakly penalized approach exhibited consistent linear convergence for k > 10 5. We notice that for both elongation/cardiac problems, the rates of convergence from all methods were not optimal as we would expect based on the error estimates [61].
20 A standard questionnaire collected data on error category and severity (based on EQUIP error classifications).
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