Summarizing p-values for high dimensional predictors

[cxd]

When assessing the significance level of the \beta parameter the z-score and associated p-value can be used in a test of significance assuming that $ \beta_j ~ N(0, v_j)$. Giving a confidence bound
$[\beta \pm z_\alpha v_j ]$
One issue with high dimensional predictors is how to assess the significance. It is desirable to communicate to others why certain attributes have a level of significance. Find methods used to assess significance of high dimensioned predictors, and examples of interpretation and communication of their significance.
For example review:
https://arxiv.org/pdf/1202.1377.pdf

[cxd]

there will be a number of possibilities for this.

Initially selection of "significant" p-values can be determined by selecting those p-values which are less than the p-value for the critical region given by the test above.

this will be the initial approach for experimenting.

However, the additional techniques of lasso regression will assist in penalising predictors that do not contribute significantly to the target variable, this will be an area to explore as well.

[cxd]

For ols a ova will provide tests of significance for explanation of variation against the null model for each addition of covariate in order of addition. For generalized model the reduction in deviance against the previous model for each additional covariate. This method would be a useful operation to add for ols and necessary to add for glm, since in the case of glm the individual p-values of covariates are only valid asymptotically.