5 citations · 27 across the 19 of their papers we have counts for
6 papers · 1 filter
Functional Bayesian Networks for Discovering Causality from Multivariate Functional Data
Fangting Zhou, Kejun He, Kunbo Wang +2
Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest, which…
Bayesian Covariate-Dependent Quantile Directed Acyclic Graphical Models for Individualized Inference
Ksheera Sagar, Yang Ni, Veerabhadran Baladandayuthapani +1
We propose an approach termed ``qDAGx'' for Bayesian covariate-dependent quantile directed acyclic graphs (DAGs) where these DAGs are individualized, in the sense that they depend…
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
Yang Ni
Causal discovery for quantitative data has been extensively studied but less is known for categorical data. We propose a novel causal model for categorical data based on a new clas…
Sparse semiparametric discriminant analysis for high-dimensional zero-inflated data
Hee Cheol Chung, Yang Ni, Irina Gaynanova
Sequencing-based technologies provide an abundance of high-dimensional biological datasets with skewed and zero-inflated measurements. Classification of such data with linear discr…
Causal Discovery with Heterogeneous Observational Data
Fangting Zhou, Kejun He, Yang Ni
We consider the problem of causal discovery (structure learning) from heterogeneous observational data. Most existing methods assume a homogeneous sampling scheme, which leads to m…
Ordinal Causal Discovery
Yang Ni, Bani Mallick
Causal discovery for purely observational, categorical data is a long-standing challenging problem. Unlike continuous data, the vast majority of existing methods for categorical da…