research
∙
07/13/2023
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Stochastic Gradient Descent (SGD) is one of the simplest and most popula...
share
research
∙
01/10/2023
High Dimensional Analysis of Variance in Multivariate Linear Regression
In this paper, we develop a systematic theory for high dimensional analy...
research
∙
08/27/2022
ℓ^2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM
We propose a new inference method for multiple change-point detection in...
research
∙
06/23/2022
Sequential Detection of Common Change in High-dimensional Data Stream
After obtaining an accurate approximation for ARL_0, we first consider t...
research
∙
04/29/2021
Testing and estimation of clustered signals
We propose a change-point detection method for large scale multiple test...
research
∙
12/15/2020
Long-term prediction intervals with many covariates
Accurate forecasting is one of the fundamental focus in the literature o...
research
∙
11/26/2020
Simultaneous inference for time-varying models
A general class of time-varying regression models is considered in this ...
research
∙
06/15/2020
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach
The broader ambition of this article is to popularize an approach for th...
research
∙
02/10/2020
A Fully Online Approach for Covariance Matrices Estimation of Stochastic Gradient Descent Solutions
Stochastic gradient descent (SGD) algorithm is widely used for parameter...
research
∙
01/28/2020