Sentence examples for maximizing the partial from inspiring English sources

The parameter estimates are obtained from maximizing the partial likelihood function (Blossfeld et al. 2007).

The β coefficient is estimated by maximizing the partial likelihood function introduced by Cox (1972).

HRs in this model were estimated by maximizing the partial likelihood, and then baseline intensity functions were calculated by the Breslow estimator.

Time-dependent covariates were used when non proportional hazards were detected, where the best-fitting model with time-varying risk coefficients are found by maximizing the partial likelihood.

Then the pseudolikelihood function which is used to determine the statistical inference is defined as follows: (1) L ˜ β = ∏ i = 1 n ∏ t exp β X i t ∑ k ∈ R ˜ i t Y k t exp β X k t d N i t In a standard Cox model, the estimate of HR can be obtained by maximizing the partial likelihood function.

The estimations β ^ e are obtained by maximizing the partial log-likelihood among the n e  patients of the reference group (3) log P ℒ e (β e ) = ∑ j = 1 n e δ j β e z j e - log ∑ k : t k ≥ t j exp (β e z k e ) where δ j  equals 1 if the failure was observed for the jth subject and 0 otherwise.

In the case of the Cox proportional hazard model, this algorithm maximizes the partial likelihood of the regression coefficients subject to a constraint imposed on the sum of the absolute value of all regression coefficients in the model.

In this section, we introduce our method to estimate the parameters in model (2.1) based on maximizing the estimated partial likelihood.

Then the parameters β can be estimated by maximizing the following estimated partial likelihood function (EPL): EPL beta)=prod_{i=1}^{n}left{frac{hat{r}_{i}(beta,S_{i})}{sum_{jin {mathcal R}(S_{i})}hat{r}_{j}(beta,S_{i})}right}^{delta_{i}}.

The optimal value of λ can be estimated through cross-validation; we chose the tuning parameter by maximizing the 10-fold cross-validated log partial likelihood.

Reservoir production optimization is a complete or partial automation process for maximizing the development effect within the lifecycle of a reservoir by optimizing operational parameters.