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Found 5 papers, 1 papers with code
Reducing the dimensionality and granularity in hierarchical categorical variables
We apply our methodology on a real dataset and find that the reduced hierarchy is an improvement over the original hierarchical structure and reduced structures proposed in the literature.
Selective inference using randomized group lasso estimators for general models
Selective inference methods are developed for group lasso estimators for use with a wide class of distributions and loss functions.
Nonparametric C- and D-vine based quantile regression
Further, we show that the nonparametric conditional quantile estimator is consistent.
Methodology
Detangling robustness in high dimensions: composite versus model-averaged estimation
We find, however, that model-averaged and composite quantile estimators often outperform least-squares methods, even in the case of Gaussian model noise.
Fixed effects testing in high-dimensional linear mixed models
A robust matching moment construction is used for creating a test that adapts to the size of the model sparsity.
