14 citations · 63 across the 16 of their papers we have counts for
9 papers · 1 filter
Signal-to-noise ratio aware minimax analysis of sparse linear regression
Shubhangi Ghosh, Yilin Guo, Haolei Weng +1
We consider parameter estimation under sparse linear regression -- an extensively studied problem in high-dimensional statistics and compressed sensing. While the minimax framework…
A note on the minimax risk of sparse linear regression
Yilin Guo, Shubhangi Ghosh, Haolei Weng +1
Sparse linear regression is one of the classical and extensively studied problems in high-dimensional statistics and compressed sensing. Despite the substantial body of literature…
Signal-to-noise ratio aware minimaxity and higher-order asymptotics
Yilin Guo, Haolei Weng, Arian Maleki
Since its development, the minimax framework has been one of the corner stones of theoretical statistics, and has contributed to the popularity of many well-known estimators, such…
Computing the degrees of freedom of rank-regularized estimators and cousins
Rahul Mazumder, Haolei Weng
Estimating a low rank matrix from its linear measurements is a problem of central importance in contemporary statistical analysis. The choice of tuning parameters for estimators re…
Optimal estimation of functionals of high-dimensional mean and covariance matrix
Jianqing Fan, Haolei Weng, Yifeng Zhou
Motivated by portfolio allocation and linear discriminant analysis, we consider estimating a functional involving both the mean vector $\mathbfμ…
On the estimation of correlation in a binary sequence model
Haolei Weng, Yang Feng
We consider a binary sequence generated by thresholding a hidden continuous sequence. The hidden variables are assumed to have a compound symmetry covariance structure with a singl…
Which bridge estimator is optimal for variable selection?
Shuaiwen Wang, Haolei Weng, Arian Maleki
We study the problem of variable selection for linear models under the high-dimensional asymptotic setting, where the number of observations grows at the same rate as the numbe…
Low noise sensitivity analysis of Lq-minimization in oversampled systems
Haolei Weng, Arian Maleki
The class of Lq-regularized least squares (LQLS) are considered for estimating a p-dimensional vector \b{eta} from its n noisy linear observations y = X\b{eta}+w. The performance o…
Overcoming The Limitations of Phase Transition by Higher Order Analysis of Regularization Techniques
Haolei Weng, Arian Maleki, Le Zheng
We study the problem of estimating from its noisy linear observations , where , under the following high-dimensional…