iSCAN: identifying causal mechanism shifts among nonlinear additive noise models
Structural causal models (SCMs) are widely used in various disciplines to represent causal
relationships among variables in complex systems. Unfortunately, the underlying causal …
relationships among variables in complex systems. Unfortunately, the underlying causal …
Simultaneous inference for pairwise graphical models with generalized score matching
Probabilistic graphical models provide a flexible yet parsimonious framework for modeling
dependencies among nodes in networks. There is a vast literature on parameter estimation …
dependencies among nodes in networks. There is a vast literature on parameter estimation …
Mean and covariance estimation for discretely observed high-dimensional functional data: Rates of convergence and division of observational regimes
A Petersen - Journal of Multivariate Analysis, 2024 - Elsevier
Estimation of the mean and covariance parameters for functional data is a critical task, with
local linear smoothing being a popular choice. In recent years, many scientific domains are …
local linear smoothing being a popular choice. In recent years, many scientific domains are …
Nonparametric and high-dimensional functional graphical models
We consider the problem of constructing nonparametric undirected graphical models for
high-dimensional functional data. Most existing statistical methods in this context assume …
high-dimensional functional data. Most existing statistical methods in this context assume …
High-dimensional functional graphical model structure learning via neighborhood selection approach
Undirected graphical models are widely used to model the conditional independence
structure of vector-valued data. However, in many modern applications, eg, those involving …
structure of vector-valued data. However, in many modern applications, eg, those involving …
Functional Directed Acyclic Graphs
In this article, we introduce a new method to estimate a directed acyclic graph (DAG) from
multivariate functional data. We build on the notion of faithfulness that relates a DAG with a …
multivariate functional data. We build on the notion of faithfulness that relates a DAG with a …
Latent multimodal functional graphical model estimation
Joint multimodal functional data acquisition, where functional data from multiple modes are
measured simultaneously from the same subject, has emerged as an exciting modern …
measured simultaneously from the same subject, has emerged as an exciting modern …
From sparse to dense functional data in high dimensions: Revisiting phase transitions from a non-asymptotic perspective
Nonparametric estimation of the mean and covariance functions is ubiquitous in functional
data analysis and local linear smoothing techniques are most frequently used. Zhang and …
data analysis and local linear smoothing techniques are most frequently used. Zhang and …
Estimation of high-dimensional differential graphs from multi-attribute data
JK Tugnait - ICASSP 2023-2023 IEEE International Conference …, 2023 - ieeexplore.ieee.org
We consider the problem of estimating differences in two Gaussian graphical models
(GGMs) which are known to have similar structure. The GGM structure is encoded in its …
(GGMs) which are known to have similar structure. The GGM structure is encoded in its …
Learning High-Dimensional Differential Graphs From Multi-Attribute Data
JK Tugnait - IEEE Transactions on Signal Processing, 2023 - ieeexplore.ieee.org
We consider the problem of estimating differences in two Gaussian graphical models
(GGMs) which are known to have similar structure. The GGM structure is encoded in its …
(GGMs) which are known to have similar structure. The GGM structure is encoded in its …