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These methods produce biased estimates of the effects of explanatory variables in the presence of unobserved confounds.
The algorithm takes care of large number of continuous as well as discrete variables in the presence of given constraints.
We consider copula modeling of the dependence between two or more random variables in the presence of a multivariate covariate.
Instrumental and mediator variables are presented as methods for isolating effects of the primary explanatory variables in the presence of such confounds.
The SFCP attempts to control the sheet thickness at 15 different points, which represent 15 output variables, using 9 manipulated variables in the presence of 3 disturbances.
We find cointegration among these variables in the presence of possible structural breaks and we overcome the problem of multicollinearity in the research design.
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In this paper, we propose an empirical log-likelihood estimator for estimating the population mean of a sensitive variable in the presence of an auxiliary variable.
The Baron and Kenny mediation model was used to assess the degree of the treatment effect upon a response variable in the presence of another variable (i.e., the mediating variable).
Overall, estimates were less variable in the presence of the temporally random stimulus than with the stationary one.
The risk of death was first examined by univariable Cox regression and then by multivariable Cox regression to adjust the hazard ratio of one variable in the presence of other variables in the multivariable model.
In addition, variables excluded in the earlier steps of the modeling were added in the final model to assess their associations with the outcome variable in the presence of the other variable or their possible confounding effect.
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