This paper considers estimation and inference for heterogeneous counterfactual effects with high‐dimensional data. We propose a novel robust score debiased of the unconditional quantile regression (Firpo, Fortin, Lemieux (2009)) as measure marginal effects. multiplier bootstrap develop asymptotic theories to guarantee size control in large sample. Simulation studies support our theories. Applyi...

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression such scenarios, including variable screening, model selection, order selection response categories, and selection. perform our gene expression with 801 patients, 2426 genes, five ...

Multidimensional Projection is a fundamental tool for high-dimensional data analytics and visualization. With very few exceptions, projection techniques are designed to map from space visual so as preserve some dissimilarity (similarity) measure, such the Euclidean distance example. In fact, although adopting distinct mathematical formulations favor different aspects of data, most multidimensio...