15 citations · 25 across the 6 of their papers we have counts for
6 papers
Estimation and Selection Properties of the LAD Fused Lasso Signal Approximator
Xiaoli Gao
The fused lasso is an important method for signal processing when the hidden signals are sparse and blocky. It is often used in combination with the squared loss function. However,…
Joint Linear Trend Recovery Using L1 Regularization
Xiaoli Gao, Ejaz Ahmed
This paper studies the recovery of a joint piece-wise linear trend from a time series using L1 regularization approach, called L1 trend filtering (Kim, Koh and Boyd, 2009). We prov…
A High-dimensional M-estimator Framework for Bi-level Variable Selection
Bin Luo, Xiaoli Gao
In high-dimensional data analysis, bi-level sparsity is often assumed when covariates function group-wisely and sparsity can appear either at the group level or within certain grou…
High-dimensional robust approximated M-estimators for mean regression with asymmetric data
Bin Luo, Xiaoli Gao
Asymmetry along with heteroscedasticity or contamination often occurs with the growth of data dimensionality. In ultra-high dimensional data analysis, such irregular settings are u…
Penalized Weighted Least Squares for Outlier Detection and Robust Regression
Xiaoli Gao, Yixin Fang
To conduct regression analysis for data contaminated with outliers, many approaches have been proposed for simultaneous outlier detection and robust regression, so is the approach…
Post Selection Shrinkage Estimation for High Dimensional Data Analysis
Xiaoli Gao, S. E. Ahmed, Yang Feng
In high-dimensional data settings where , many penalized regularization approaches were studied for simultaneous variable selection and estimation. However, with the existe…