48 citations · 67 across the 7 of their papers we have counts for
10 papers · 1 filter
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…
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…
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 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…
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…