48 citations · 122 across the 27 of their papers we have counts for
27 papers
Latent Subgroup Identification in Image-on-scalar Regression
Zikai Lin, Yajuan Si, Jian Kang
Image-on-scalar regression has been a popular approach to modeling the association between brain activities and scalar characteristics in neuroimaging research. The associations co…
Bayesian inference for group-level cortical surface image-on-scalar-regression with Gaussian process priors
Andrew S. Whiteman, Timothy D. Johnson, Jian Kang
In regression-based analyses of group-level neuroimage data researchers typically fit a series of marginal general linear models to image outcomes at each spatially-referenced pixe…
Statistical inferences for complex dependence of multimodal imaging data
Jinyuan Chang, Jing He, Jian Kang +1
Statistical analysis of multimodal imaging data is a challenging task, since the data involves high-dimensionality, strong spatial correlations and complex data structures. In this…
Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks
Daiwei Zhang, Tianci Liu, Jian Kang
Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of supervised learning applications. However, accurately quantifying the uncerta…
Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis
Bingyuan Liu, Qi Zhang, Lingzhou Xue +2
It is of importance to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible outliers in real-world applications s…
Deep Neural Networks Guided Ensemble Learning for Point Estimation
Tianyu Zhan, Haoda Fu, Jian Kang
In modern statistics, interests shift from pursuing the uniformly minimum variance unbiased estimator to reducing mean squared error (MSE) or residual squared error. Shrinkage base…
Bayesian Inference for Brain Activity from Functional Magnetic Resonance Imaging Collected at Two Spatial Resolutions
Andrew S. Whiteman, Andreas J. Bartsch, Jian Kang +1
Neuroradiologists and neurosurgeons increasingly opt to use functional magnetic resonance imaging (fMRI) to map functionally relevant brain regions for noninvasive presurgical plan…
Bayesian Hierarchical Models for High-Dimensional Mediation Analysis with Coordinated Selection of Correlated Mediators
Yanyi Song, Xiang Zhou, Jian Kang +9
We consider Bayesian high-dimensional mediation analysis to identify among a large set of correlated potential mediators the active ones that mediate the effect from an exposure va…
Statistical Inference for High-Dimensional Vector Autoregression with Measurement Error
Xiang Lyu, Jian Kang, Lexin Li
High-dimensional vector autoregression with measurement error is frequently encountered in a large variety of scientific and business applications. In this article, we study statis…
Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects
Yanyi Song, Xiang Zhou, Jian Kang +9
Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of int…
Deep Historical Borrowing Framework to Prospectively and Simultaneously Synthesize Control Information in Confirmatory Clinical Trials with Multiple Endpoints
Tianyu Zhan, Yiwang Zhou, Ziqian Geng +5
In current clinical trial development, historical information is receiving more attention as it provides utility beyond sample size calculation. Meta-analytic-predictive (MAP) prio…
Image Response Regression via Deep Neural Networks
Daiwei Zhang, Lexin Li, Chandra Sripada +1
Delineating the associations between images and a vector of covariates is of central interest in medical imaging studies. To tackle this problem of image response regression, we pr…
Minorization-Maximization-based Steepest Ascent for Large-scale Survival Analysis with Time-Varying Effects: Application to the National Kidney Transplant Dataset
Kevin He, Ji Zhu, Jian Kang +1
The time-varying effects model is a flexible and powerful tool for modeling the dynamic changes of covariate effects. However, in survival analysis, its computational burden increa…
Bayesian Symbolic Regression
Ying Jin, Weilin Fu, Jian Kang +2
Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machi…
Optimizing Graphical Procedures for Multiplicity Control in a Confirmatory Clinical Trial via Deep Learning
Tianyu Zhan, Alan H Hartford, Jian Kang +1
In confirmatory clinical trials, it has been proposed to use a simple iterative graphical approach to construct and perform intersection hypotheses tests with a weighted Bonferroni…
Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior
Qingpo Cai, Jian Kang, Tianwei Yu
Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur he…
Covariance-Insured Screening
Kevin He, Jian Kang, Hyokyoung Grace Hong +5
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarke…
SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting
Emily Morris, Kevin He, Yanming Li +2
High-dimensional variable selection in the proportional hazards (PH) model has many successful applications in different areas. In practice, data may involve confounding variables…
Classification with Ultrahigh-Dimensional Features
Yanming Li, Hyokyoung Hong, Jian Kang +3
Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensi…
A Network Object Method to Uncover Hidden Disorder-Related Brain Connectome
Shuo Chen, Yishi Xing, Jian Kang +3
Neuropsychiatric disorders impact functional connectivity of the brain at the network level. The identification and statistical testing of disorder-related networks remains challen…
Scalar-on-Image Regression via the Soft-Thresholded Gaussian Process
Jian Kang, Brian J. Reich, Ana-Maria Staicu
The focus of this work is on spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models, soft-thresholded Gaussian processes…
Conditional Screening for Ultra-high Dimensional Covariates with Survival Outcomes
Hyokyoung Grace Hong, Jian Kang, Yi Li
Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a…
High Dimensional Tests for Functional Networks of Brain Anatomic Regions
Jichun Xie, Jian Kang
There has been increasing interests in learning resting-state brain functional connectivity of autism disorders using functional magnetic resonance imaging (fMRI) data. The data in…
Network induced large correlation matrix estimation
Shuo Chen, Jian Kang, Yishi Xing +2
The correlation matrix of massive biomedical data (e.g. gene expression or neuroimaging data) often exhibits a complex and organized, yet latent graph topological structure. We pro…