Sentence examples for make no distributional from inspiring English sources

We assume some "regularity conditions", but make no distributional assumptions about the misclassified variable or the measurement error.

Some classifiers make no distributional assumptions but use the local vicinity of an object to compute the probability of class membership.

The DeLong et al. method and the U-statistics based method are nonparametric and make no distributional assumption on the biomarker data in computing the AUC and its variance.

So, in theory, computer intensive methods such as the bootstrap that make no distributional assumptions may be more appropriate for estimating sample size and analysing HRQoL data than conventional statistical methods.

So, in theory, computer intensive methods such as the bootstrap that make no distributional assumptions may therefore be more appropriate for estimating sample size and analysing HRQoL data than conventional statistical methods.

Non-linear machine learning algorithms are more effective in generating prediction signatures, as opposed to linear regression, because they make no distributional assumptions about the predictor variables and allow all features, including those with individually weak effects, to contribute to the model fit.

The more sophisticated semiparametric method makes no distributional assumptions, and is therefore able to reveal a distribution structure that may differ from the Gaussian normal distribution.

Our approach requires GWAS summary data only and makes no distributional assumption on the causal variant effect sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples.

The semiparametric KS estimator makes no distributional assumptions, but it assumes a linear index function to avoid the curse of dimensionality.

22 CHAID is a non-parametric technique that makes no distributional assumptions on outliers, collinearities, heteroscedasticity or distributional error structures.

This approach is a robust method that makes no distributional assumption about the error term in a model.