Arts & Sciences Events

Location

 Title: Sufficient dimension folding in regression via distance covariance for matrix?valued predictors

Abstract: In modern data, when predictors are matrix/array?valued, building a reasonable model is much more difficult due to the complicate structure. However, dimension folding that reduces the predictor dimensions while keeps its structure is critical in helping to build a useful model. In this paper, we develop a new sufficient dimension folding method using distance covariance for regression in such a case. The method works efficiently without strict assumptions on the predictors. It is model?free and nonparametric, but neither smoothing techniques nor selection of tuning parameters is needed. Moreover, it works for both univariate and multivariate response cases. In addition, we propose a new method of local search to estimate the structural dimensions. Simulations and real data analysis support the efficiency and effectiveness of the proposed method.

Join Zoom Meeting
https://cwm.zoom.us/j/91822743229?pwd=VGdTbFF5VVFoNnlLTVFjWG9aa3BWdz09

Meeting ID: 918 2274 3229
Passcode: 123456

Contact

GuanNan Wang

This Event Appears On

• W&M Featured Events
• Arts & Sciences Events
• Computer Science Events
• Mathematics Events
• Data Science