5 papers
Global Inference for High Dimensional Quantile Regression with Heterogeneity
Tate Jacobson, Daeyoung Ham, Ben Sherwood
Quantile regression has received considerable attention in high dimensional methodology due to its robustness and ability to accommodate heteroskedasticity. Despite this attention,…
Large-scale linear hypothesis testing for high-dimensional online M-estimation
Daeyoung Ham, Myeonghun Yu, Tate Jacobson
With the growing prevalence of streaming data, renewable estimation has become an important tool for updating statistical analyses without retaining historical raw observations. We…
Nonparametric Envelopes for Flexible Response Reduction
Tate Jacobson
Envelope methods improve the estimation efficiency in multivariate linear regression by identifying and separating the material and immaterial parts of the responses or the predict…
Envelope-Guided Regularization for Improved Prediction in High-Dimensional Multivariate Regression
Tate Jacobson, Oh-Ran Kwon
Envelope methods perform dimension reduction of predictors or responses in multivariate regression, exploiting the relationship between them to improve estimation efficiency. While…
Strong Oracle Guarantees for Partial Penalized Tests of High Dimensional Generalized Linear Models
Tate Jacobson
Partial penalized tests provide flexible approaches to testing linear hypotheses in high dimensional generalized linear models. However, because the estimators used in these tests…