Sentence examples for procedures for binary from inspiring English sources

Moore [ 8] evaluates discrimination procedures for binary data.

The following uses the natural logarithm scale to carry out such calculations, similar to statistical procedures for binary effect measures (risk ratio and odds ratio), due to its desirable statistical properties [ 14].

Multiple imputation (n = 100 imputations) [ 18] was performed using mi logit procedure for binary outcomes and mi ologit procedure for ordinal outcomes [ 17] with the following variables used in imputation: corresponding baseline values; participant gender; participant age at baseline; deprivation index; and randomisation group.

Odds ratios point estimates (OR) and their 95% confidence intervals (95% CIs) were computed by MLR procedure for binary data to estimate the association between each covariate levels and HPV infections while adjusting for the effect of other variables retained in the model.

A general process design procedure for a binary feed solution is outlined.

Results are compared with a collision-based chemistry procedure for two binary reactions in a 1-D unsteady shock-expansion tube simulation.

Frege (and Russell) devised an ingenious procedure for regimenting binary quantifiers like "every" and "some" in terms of unary quantifiers like "everything" and "something": they formalized sentences of the form $\ulcorner$Some $A$ is $B$$\urcorner$ and $\ulcorner$Every $A$ is $B$$\urcorner$ as $\exists x (Ax \wedge Bx)$ and $\forall x (Ax \rightarrow Bx)$, respectively.

Two-step etching procedures have been developed for binary polymer blends of linear low density polyethylene (LLDPE) with high density polyethylene (HDPE), and for blends of atactic and syndiotactic polystyrene.

Small GDT trials are particularly vulnerable to bias, and investigators may find it difficult to demonstrate the adequacy of their procedures for assessing non-binary outcomes such as complications.

The logistic procedure fits linear logistic regression models for binary or ordinal response data using Maximum Likelihood estimations and compares the estimated samples whereas artificial neural network systems attempts to assign proper weights to the respective inputs by a 'genetic algorithm' optimization procedure to allow for the correct deduction of the ultimate outcome.

The SAS procedures MIXED (for continuous variables) and GLIMMIX (for binary variables) with the REML (restricted maximum likelihood) estimation were used with adjustment for baseline and including a time by treatment interaction term.