68 citations · 210 across the 41 of their papers we have counts for
43 papers
Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relations
Jie Jian, Jiguo Cao, Owen G. Ward
We study continuous-time relational event data, where time-stamped dyadic interactions reflect both individual node propensities and evolving relational proximity. We propose a dyn…
Functional Attentive Interpretable Regression
Haixu Wang, Tianyu Guan, Jiguo Cao
In function-on-function regression, the coefficient surface may exhibit complex support structure---from localized patches to global patterns such as disconnected regions,…
Estimation of Functional Principal Components from Sparse Functional Data
Uche Mbaka, Jiguo Cao, Michelle Carey
Sparse functional data arise when measurements are observed infrequently and at irregular time points for each subject, often in the presence of measurement error. These characteri…
Learning Data-Efficient and Generalizable Neural Operators via Fundamental Physics Knowledge
Siying Ma, Mehrdad M. Zadeh, Mauricio Soroco +3
Recent advances in scientific machine learning (SciML) have enabled neural operators (NOs) to serve as powerful surrogates for modeling the dynamic evolution of physical systems go…
Locally sparse estimation for simultaneous functional quantile regression
Boyi Hu, Jiguo Cao
Motivated by the study of how daily temperature affects soybean yield, this article proposes a simultaneous functional quantile regression (FQR) model featuring a locally sparse bi…
Convolution-smoothing based locally sparse estimation for functional quantile regression
Hua Liu, Boyi Hu, Jinhong You +1
Motivated by an application to study the impact of temperature, precipitation and irrigation on soybean yield, this article proposes a sparse semi-parametric functional quantile mo…
Dynamic Survival Prediction using Longitudinal Images based on Transformer
Bingfan Liu, Haolun Shi, Jiguo Cao
Survival analysis utilizing multiple longitudinal medical images plays a pivotal role in the early detection and prognosis of diseases by providing insight beyond single-image eval…
Two Sample Testing for High-dimensional Functional Data: A Multi-resolution Projection Method
Shouxia Wang, Jiguo Cao, Hua Liu +2
It is of great interest to test the equality of the means in two samples of functional data. Past research has predominantly concentrated on low-dimensional functional data, a focu…
Online Learning of Functional Principal Component Analysis for Multidimensional Functional Data
Muye Nanshan, Nan Zhang, Jiguo Cao
Multidimensional functional data streams arise in diverse scientific fields, yet their analysis poses significant challenges. We propose a novel online framework for functional pri…
Probabilistic Functional Neural Networks
Haixu Wang, Jiguo Cao
High-dimensional functional time series (HDFTS) are often characterized by nonlinear trends and high spatial dimensions. Such data poses unique challenges for modeling and forecast…
Similarity-Informed Transfer Learning for Multivariate Functional Censored Quantile Regression
Hua Liu, Jiaqi Men, Shouxia Wang +2
To address the challenge of utilizing patient data from other organ transplant centers (source cohorts) to improve survival time estimation and inference for a target center (targe…
Representation learning of dynamic networks
Haixu Wang, Jiguo Cao, Jian Pei
This study presents a novel representation learning model tailored for dynamic networks, which describes the continuously evolving relationships among individuals within a populati…
An Efficient Two-Dimensional Functional Mixed-Effect Model Framework for Repeatedly Measured Functional Data
Cheng Cao, Jiguo Cao, Hao Pan +3
With the rapid development of wearable device technologies, accelerometers can record minute-by-minute physical activity for consecutive days, which provides important insight into…
Functional Autoencoder for Smoothing and Representation Learning
Sidi Wu, Cédric Beaulac, Jiguo Cao
A common pipeline in functional data analysis is to first convert the discretely observed data to smooth functions, and then represent the functions by a finite-dimensional vector…
Robust Bayesian Functional Principal Component Analysis
Jiarui Zhang, Jiguo Cao, Liangliang Wang
We develop a robust Bayesian functional principal component analysis (RB-FPCA) method that utilizes the skew elliptical class of distributions to model functional data, which are o…
Generalized Bayesian Multidimensional Scaling and Model Comparison
Jiarui Zhang, Jiguo Cao, Liangliang Wang
Multidimensional scaling (MDS) is widely used to reconstruct a low-dimensional representation of high-dimensional data while preserving pairwise distances. However, Bayesian MDS ap…
A Gaussian Sliding Windows Regression Model for Hydrological Inference
Stefan Schrunner, Parham Pishrobat, Joseph Janssen +4
Statistical models are an essential tool to model, forecast and understand the hydrological processes in watersheds. In particular, the understanding of time lags associated with t…
Functional Adaptive Double-Sparsity Estimator for Functional Linear Regression Model with Multiple Functional Covariates
Cheng Cao, Jiguo Cao, Hailiang Wang +2
Sensor devices have been increasingly used in engineering and health studies recently, and the captured multi-dimensional activity and vital sign signals can be studied in associat…
Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science
Joseph Janssen, Shizhe Meng, Asad Haris +5
Scientists and statisticians often want to learn about the complex relationships that connect two time-varying variables. Recent work on sparse functional historical linear models…
A Joint Estimation Approach to Sparse Additive Ordinary Differential Equations
Nan Zhang, Muye Nanshan, Jiguo Cao
Ordinary differential equations (ODEs) are widely used to characterize the dynamics of complex systems in real applications. In this article, we propose a novel joint estimation ap…
Neural Networks for Scalar Input and Functional Output
Sidi Wu, Cédric Beaulac, Jiguo Cao
The regression of a functional response on a set of scalar predictors can be a challenging task, especially if there is a large number of predictors, or the relationship between th…
Random Competing Risks Forests for Large Data
Joel Therrien, Jiguo Cao
Random forests are a sensible non-parametric model to predict competing risk data according to some covariates. However, there are currently no packages that can adequately handle…
Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics
Cédric Beaulac, Sidi Wu, Erin Gibson +5
A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem…
Functional Nonlinear Learning
Haixu Wang, Jiguo Cao
Using representations of functional data can be more convenient and beneficial in subsequent statistical models than direct observations. These representations, in a lower-dimensio…