Sentence examples for model selection includes from inspiring English sources

The process of model selection includes how to select covariates (eg, meteorological variables and co-pollutants), lag structure for air pollutants and the number of degrees of freedom for smoothing functions to adjust for long-term trend, short fluctuation, seasonality, other covariates and the determination of referent in case-crossover design.

Model selection includes the best possible selection of variables to come up with the best statistical model for given variable set.

Criteria for model selection included both discriminant ability (defined by the AUC) and model simplicity.

Various criteria can be used for model selection, including various information-theoretic criteria [ 23].

For each endpoint, the initial stage of model selection included all such possible confounders as described above.

Further studies using model selection (including or not interaction terms) in generalized additive modelling (Wood, 2006) would certainly allow a more refined analysis.

We performed multivariate logistic regression, employing forward selection and 10-fold cross validation for model selection including estimation of two measures of prediction accuracy: the misclassification rate and the area under the receiver operating characteristic curve (AUC).

Other ways to improve the efficiency of analyzing many QTL effects with Bayesian model selection include specifying prior inclusion probability for epistasis and using Metropolis/Hastings algorithm to perform fast sampling for binary indicator (Yi et al., 2007).

jMT2 uses PhyML (Guindon and Gascuel, 2003) to obtain maximum likelihood estimates of model parameters, and implements different statistical criteria for model selection including hierarchical and dynamical likelihood ratio tests, Akaike's and Bayesian information criteria (AIC and BIC) and a performance-based decision theory method (Posada and Buckley, 2004).

> -wrap-foot> Each of the three models with selection included a parameter that reflected selection strength: the inverse shape parameter in model B, the fraction of slowly evolving genes in model C, and the ratio of the population size to the genome size in model D (see Materials and Methods for details).

Analysis was also performed using a model selection that included all covariates.