no code implementations • 11 Dec 2022 • Rebeka Man, Kean Ming Tan, Zian Wang, Wen-Xin Zhou
In this paper, we propose and study (penalized) robust expectile regression (retire), with a focus on iteratively reweighted $\ell_1$-penalization which reduces the estimation bias from $\ell_1$-penalization and leads to oracle properties.
no code implementations • 25 Oct 2021 • Kean Ming Tan, Heather Battey, Wen-Xin Zhou
We address the problem of how to achieve optimal inference in distributed quantile regression without stringent scaling conditions.
1 code implementation • 9 Dec 2020 • Xuming He, Xiaoou Pan, Kean Ming Tan, Wen-Xin Zhou
Our numerical studies confirm the conquer estimator as a practical and reliable approach to large-scale inference for quantile regression.
Statistics Theory Methodology Statistics Theory
no code implementations • 9 Dec 2020 • Yanxin Jin, Yang Ning, Kean Ming Tan
Motivated by functional magnetic resonance imaging (fMRI) studies, we propose a novel method for constructing brain connectivity networks with correlated replicates and latent effects.
Methodology
no code implementations • 15 May 2020 • Jiaying Zhou, Jie Ding, Kean Ming Tan, Vahid Tarokh
The main crux is to sequentially incorporate additional learners that can enhance the prediction accuracy of an existing joint model based on user-specified parameter sharing patterns across a set of learners.
no code implementations • 28 May 2019 • Kean Ming Tan, Junwei Lu, Tong Zhang, Han Liu
To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story.
no code implementations • 18 Oct 2018 • Kean Ming Tan, Qiang Sun, Daniela Witten
We propose robust sparse reduced rank regression for analyzing large and complex high-dimensional data with heavy-tailed random noise.
no code implementations • 17 Sep 2018 • Kean Ming Tan, Zhaoran Wang, Tong Zhang, Han Liu, R. Dennis Cook
Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of information on the conditional distribution of the response given the covariates.
no code implementations • ICML 2018 • Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang
Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.
no code implementations • 4 Jun 2017 • Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang
Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.
no code implementations • 29 Apr 2016 • Kean Ming Tan, Zhaoran Wang, Han Liu, Tong Zhang
Sparse generalized eigenvalue problem (GEP) plays a pivotal role in a large family of high-dimensional statistical models, including sparse Fisher's discriminant analysis, canonical correlation analysis, and sufficient dimension reduction.
no code implementations • 27 Apr 2016 • Lei Han, Kean Ming Tan, Ting Yang, Tong Zhang
A major challenge for building statistical models in the big data era is that the available data volume far exceeds the computational capability.
no code implementations • 16 May 2014 • Kean Ming Tan, Noah Simon, Daniela Witten
Many authors have proposed methods to reduce the effects of selection bias under the assumption that the naive estimates of the effect sizes are independent.
no code implementations • 28 Feb 2014 • Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, Daniela Witten
We consider the problem of learning a high-dimensional graphical model in which certain hub nodes are highly-connected to many other nodes.
no code implementations • 19 Jul 2013 • Kean Ming Tan, Daniela Witten, Ali Shojaie
We begin by introducing a surprising connection between the graphical lasso and hierarchical clustering: the graphical lasso in effect performs a two-step procedure, in which (1) single linkage hierarchical clustering is performed on the variables in order to identify connected components, and then (2) an l1-penalized log likelihood is maximized on the subset of variables within each connected component.