Sentence examples for irrelevant regression from inspiring English sources

Parameter A can be estimated using two non-linear irrelevant regression condition equations, including (1) and (19): {mathrm{bm}}_{mathrm{i},mathrm{t}}={sum}_{mathrm{j}=1}^{infty }{mathrm{k}}_1^{mathrm{j}-1}left({mathrm{E}}_{mathrm{t}}left[{mathrm{r}}_{mathrm{i},mathrm{t}+mathrm{j}}right]-{mathrm{E}}_{mathrm{t}}left[{mathrm{r}mathrm{oe}}_{mathrm{i},mathrm{t}+mathrm{j}}right]right) (19).

The order of entry into models is irrelevant in simultaneous regressions (unlike in hierarchical regression).

From an econometric perspective, this may reduce the chance of including irrelevant variables in a regression model and the resulting efficiency of estimates but comes at a cost of placing strong behavioral assumptions on the components of the score.29 After all, the score is just a linear combination and implicitly makes assumptions about relative substitutability of effects of different SNPs.

Third, all clinically irrelevant causal associations (standardised regression coefficients below 0.1) were excluded, and the model was re-calculated.

Another crucial point is the assumption of inclusion of all relevant variables and exclusion of all irrelevant ones in the regression model.

Although lasso is more successful in setting the regression coefficients of irrelevant SNPs to 0 than the ridge regression and the CCA-based method, it still finds many SNPs as having a non-zero association strength.

In association mapping, we typically expect a small number of loci to be associated with the phenotype, and lasso provides an effective tool to identify those relevant SNPs and set the regression coefficients for irrelevant SNPs to zero (Wu et al., 2009).

All regression coefficients corresponding to irrelevant predictors were set to 0.0.

Preventing the development of new vasculature is irrelevant and unlikely to lead to regression of the lesions.

If the proportion of all deaths due to other causes is either very large or very small, use of the competing risks method may be irrelevant; in other circumstances competing risks regression is both appropriate and necessary.

Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned using ridge regression, elastic net and lasso methods; hence, the architectural design of ELM network can be automated.