Concentration inequalities for statistical inference
H Zhang, SX Chen - arXiv preprint arXiv:2011.02258, 2020 - arxiv.org
This paper gives a review of concentration inequalities which are widely employed in non-
asymptotical analyses of mathematical statistics in a wide range of settings, from distribution …
asymptotical analyses of mathematical statistics in a wide range of settings, from distribution …
Oracle Inequality for Sparse Trace Regression Models with Exponential β-mixing Errors
In applications involving, eg, panel data, images, genomics microarrays, etc., trace
regression models are useful tools. To address the high-dimensional issue of these …
regression models are useful tools. To address the high-dimensional issue of these …
The EAS approach for graphical selection consistency in vector autoregression models
As evidenced by various recent and significant papers within the frequentist literature, along
with numerous applications in macroeconomics, genomics, and neuroscience, there …
with numerous applications in macroeconomics, genomics, and neuroscience, there …
High-dimensional inference for linear model with correlated errors
P Yuan, X Guo - Metrika, 2022 - Springer
Temporally correlated error process is commonly encountered in practice and poses
significant challenges in high-dimensional statistical analysis. This paper conducts low …
significant challenges in high-dimensional statistical analysis. This paper conducts low …
Lasso regression in sparse linear model with -mixing errors
L Peng, Y Zhu, W Zhong - Metrika, 2023 - Springer
This paper investigates the Lasso method for sparse linear models with exponential φ-
mixing errors under a fixed design, where the number of covariates p is large, or even much …
mixing errors under a fixed design, where the number of covariates p is large, or even much …