no code implementations • 20 May 2024 • Muxuan Liang, Yang Ning, Maureen A Smith, Ying-Qi Zhao
In this work, we propose a kernel-smoothed decorrelated score to construct hypothesis testing and interval estimations for the linear decision rule estimated using a piece-wise linear surrogate loss, which has a discontinuous gradient and non-regular Hessian.
no code implementations • 31 Aug 2022 • Jingyi Duan, Yang Ning, Xi Chen, Yong Chen
In many scenarios such as genome-wide association studies where dependences between variables commonly exist, it is often of interest to infer the interaction effects in the model.
no code implementations • 29 Jul 2022 • Kevin Jiang, Yang Ning
The estimation of the treatment effect is often biased in the presence of unobserved confounding variables which are commonly referred to as hidden variables.
no code implementations • 24 Dec 2021 • Inbeom Lee, Siyi Deng, Yang Ning
To extract the information from the dependence structure for clustering, we propose a new latent variable model for the features arranged in matrix form, with some unknown membership matrices representing the clusters for the rows and columns.
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 • 28 Nov 2020 • Siyi Deng, Yang Ning, Jiwei Zhao, Heping Zhang
Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis.
no code implementations • 7 Sep 2020 • Yang Ning, Sida Peng, Jing Tao
This paper proposes a doubly robust two-stage semiparametric difference-in-difference estimator for estimating heterogeneous treatment effects with high-dimensional data.
no code implementations • 17 Feb 2020 • Huijie Feng, Chunpeng Wu, Guoyang Chen, Weifeng Zhang, Yang Ning
In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier.
no code implementations • 26 May 2019 • Huijie Feng, Yang Ning, Jiwei Zhao
Statistically, we show that the finite sample error bound for estimating $\theta$ in $\ell_2$ norm is $(s\log d/n)^{\beta/(2\beta+1)}$, where $d$ is the dimension of $\theta$, $s$ is the sparsity level, $n$ is the sample size and $\beta$ is the smoothness of the conditional density of $X$ given the response $Y$ and the covariates $Z$.
no code implementations • 20 Dec 2018 • Yang Ning, Sida Peng, Kosuke Imai
We first use a class of penalized M-estimators for the propensity score and outcome models.
no code implementations • 13 Jun 2018 • Carson Eisenach, Florentina Bunea, Yang Ning, Claudiu Dinicu
We employ model assisted clustering, in which the clusters contain features that are similar to the same unobserved latent variable.
no code implementations • 23 Apr 2017 • Xin Bing, Florentina Bunea, Yang Ning, Marten Wegkamp
This work introduces a novel estimation method, called LOVE, of the entries and structure of a loading matrix A in a sparse latent factor model X = AZ + E, for an observable random vector X in Rp, with correlated unobservable factors Z \in RK, with K unknown, and independent noise E. Each row of A is scaled and sparse.
no code implementations • NeurIPS 2015 • Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu
We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models.
no code implementations • 30 Oct 2015 • Matey Neykov, Yang Ning, Jun S. Liu, Han Liu
Our main theoretical contribution is to establish a unified Z-estimation theory of confidence regions for high dimensional problems.
no code implementations • 9 Feb 2015 • Quanquan Gu, Yuan Cao, Yang Ning, Han Liu
Due to the presence of unknown marginal transformations, we propose a pseudo likelihood based inferential approach.
no code implementations • 30 Dec 2014 • Yang Ning, Han Liu
Specifically, we propose a decorrelated score function to handle the impact of high dimensional nuisance parameters.
no code implementations • 30 Dec 2014 • Zhuoran Yang, Yang Ning, Han Liu
We propose a new class of semiparametric exponential family graphical models for the analysis of high dimensional mixed data.
no code implementations • 30 Dec 2014 • Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu
We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models.
no code implementations • 16 Dec 2014 • Ethan X. Fang, Yang Ning, Han Liu
This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models.
no code implementations • 6 Dec 2014 • Yang Ning, Tianqi Zhao, Han Liu
(i) We develop a regularized statistical chromatography approach to infer the parameter of interest under the proposed semiparametric generalized linear model without the need of estimating the unknown base measure function.
1 code implementation • 29 Apr 2014 • Jianqing Fan, Han Liu, Yang Ning, Hui Zou
Theoretically, the proposed methods achieve the same rates of convergence for both precision matrix estimation and eigenvector estimation, as if the latent variables were observed.