Sentence examples for by maximizing the partial from inspiring English sources
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.
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.
Secondly, with the partial CSIT knowledge we derive suboptimal precoders by maximizing the average MI.
Basically, factors F S = S W S and F T = T W T are linear combinations of SNP variables and transcript variables, where the weight values W S and W T in the linear combinations can be calculated iteratively by maximizing the covariance of F S and F T. The maximization procedure can be performed using a nonlinear iterative partial least squares algorithm.
