SPEED 3: Statistical Methods for High Dimensional and Complex Data , Part 1 -- JSM 2025

$S^1$-Indexed Brownian Motion Through Abstract Wiener Spaces

10:35 AM - 10:40 AM

Co-authors: Nicolas Escobar, Chunfeng Huang (Indiana University), Nicolas Escobar

SPDE Approach Brownian motion Matérn covariance

A Multiple Imputation for Binary and Ordinal Responses: A Multivariate Probit Model Approach

10:40 AM - 10:45 AM

Co-authors: Stephen Kofi Acheampong, Stephen Kofi Acheampong

Multiple Imputation Multinomial probit model Markov chain Monte Carlo (MCMC)Missing completely at random (MCAR)Missing at random (MAR)Missing not at random (MNAR).

Advancing Ultra-high-dimensional Functional Regression: Exploring Genome-wide Association Studies

10:45 AM - 10:50 AM

Co-authors: Wanying Zhu, Lily Wang (George Mason University), Wanying Zhu, Guannan Wang (College of William and Mary)

Bayesian Information Criterion Functional linear model Bivariate splines Forward selection GWAS

An R Package for Multivariate Penalized Splines on Triangulations with Global & Distributed Learning

10:50 AM - 10:55 AM

Co-authors: YU-CHUN WANG (George Mason University), Lily Wang (George Mason University), YU-CHUN WANG (George Mason University), Guannan Wang (College of William and Mary)

Complex multidimensional data Computational efficiency Distributed learning Nonparametric smoothing Multivariate spline smoothing MPST package

Analysis of Multivariate Binary Data Using D-Vine Copula Model

10:55 AM - 11:00 AM

Co-authors: Huihui Lin (Hampton University), N. Rao Chaganty (Old Dominion University), N. Rao Chaganty (Old Dominion University)

Multivariate Binary Copula D-Vine

WITHDRAWN Automatic Calibration of Agent-Based Models using Recurrent Neural Networks

11:00 AM - 11:05 AM

Co-authors: sima Najafzadehkhoei, George Vega Yon (University of Utah), Yue Zhang (University of Utah), Bernardo Modenesi (University of Utah)

Parameter Calibration Agent-Based Models (ABMs) Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Susceptible-Infected-Recovered (SIR) Model Parameter Calibration Recurrent Neural Networks (RNNs)

Consensus Dimension Reduction via Data Integration

11:05 AM - 11:10 AM

Co-authors: Bingxue An (University of Notre Dame), Bingxue An (University of Notre Dame), Tiffany Tang (University of Notre Dame)

dimension reduction data integration data visualization

High-Dimensional Graphical Latent Gaussian Copula Model with Covariates

11:10 AM - 11:15 AM

Co-authors: Zhentao Yu, Zhentao Yu

Copula Model High Dimensional Data Graphical Model Precision Matrix Estimation Sparse Group Lasso Covariate-Dependent Networks

ImgKnock: Novel Knockoff Generation and Feature Selection for Image Data via Latent Representations

11:15 AM - 11:20 AM

Co-authors: Jericho Lawson (University of California, Riverside), Jericho Lawson (University of California, Riverside), Zhe Fei (University of California, Riverside)

knockoff selection latent representation learning FDR control fundus images self-supervised learning

Integration of Image Segmentation with Classical L<sub>2</sub> Optimization Theories of Statistics

11:20 AM - 11:25 AM

Co-authors: Jiwoong Kim (Department of Mathematics and Statistics University of South Florida), Jinhee Jang (Seoul St. Mary Hospital, College of Medicine, The Catholic University of Kor), Jiwoong Kim (Department of Mathematics and Statistics University of South Florida), Kun Bu (Department of Mathematics and Statistics)

Empirical distribution Cramer-von Mises magnetic resonance minimum distance segmenting together

Interpretable Deep Learning with Scalable Kernel-Based Density Estimation

11:30 AM - 11:35 AM

Co-authors: Ayyuce Begum Bektas (Memorial Sloan Kettering Cancer Center), Ayyuce Begum Bektas (Memorial Sloan Kettering Cancer Center), Mithat Gonen (Memorial Sloan-Kettering Cancer Center)

interpretable deep learning machine learning learning with kernels random features nonparametric conditional density estimation

Joint Graphical Lasso with Regularized Aggregation

11:35 AM - 11:40 AM

Co-authors: Jongik Chung (University of Central Florida), Jongik Chung (University of Central Florida), Qihu Zhang (University of Georgia), Jennifer McDowell (University of Georgia), Cheolwoo Park (KAIST)

Aggregation Brain connectivity Joint estimation Precision matrix estimation Regularization Long-memory

Localized Sparse Principal Component Analysis of Multivariate Time Series in Frequency Domain

11:40 AM - 11:45 AM

Co-authors: Jamshid Namdari, Robert Krafty (Emory University), Jamshid Namdari, Amita Manatunga (Emory University), Fabio Ferrarelli (University of Pittsburgh)

Principal Component Analysis High Dimensional Time Series Spectral Density Matrix Sparse Estimation EEG Data

Merging Versus Ensembling: An Adaptive Blending Approach for Handling Domain Heterogeneity

11:45 AM - 11:50 AM

Co-authors: Daniel Kojis, Prasad Patil (Boston University), Daniel Kojis, Kevin Lane (Boston University - Department of Environmental Health)

machine learning domain generalization ensemble learning multi-study prediction

Modification to the LASSO Regression Model via its Bayesian Interpretation

11:50 AM - 11:55 AM

Co-authors: Gayan Warahena Liyanage (University of Dayton), Gayan Warahena Liyanage (University of Dayton), Felix Famoye (Central Michigan University), Carl Lee (Central Michigan University)

LASSO regression beta-Laplace distribution T-Laplace family Variable selection Prediction

Nonparametric Estimation of Spatial Covariance Function using Mixtures of Gaussian Kernels

11:55 AM - 12:00 PM

Co-authors: Kanahela Muhandiramge Manushi Hansani Siriwardana, Kanahela Muhandiramge Manushi Hansani Siriwardana, Hyebin Song (Penn State), Stephen Berg

Stationary isotropic processes Spatial covariance function Nonparametric estimation Gaussian mixtures Fast computation

Self-normalization Tests for Change Points in Functional Time Series

12:00 PM - 12:05 PM

Co-authors: Zhiyuan Du, Zhiyuan Du, Pang Du (Virginia Tech)

Change point detection Functional time series Self-normalization Non-overlapping block bootstrap

Semi-parametric Spatial Intensity Estimation with Bandwidth Selection on KDE-NN based model

12:05 PM - 12:10 PM

Co-authors: Zhiwen Wang (New Jersey Insititute of Technology), Zhiwen Wang (New Jersey Insititute of Technology), Ji Meng Loh (New Jersey Institute of Technology)

Spatial Intensity Estimation Deep Learning Model Bandwidth Selection Kernel Density Estimation

Sparse-Input Neural Network using Group Concave Regularization

12:10 PM - 12:15 PM

Co-authors: Bin Luo (Kennesaw State University), Bin Luo (Kennesaw State University), Susan Halabi (Duke University)

Neural networks Feature selection High dimensionality LASSO nonconvex penalty

Visualize your fitted non-linear dimension reduction model in the high-dimensional data space

12:15 PM - 12:20 PM

Co-authors: Piyadi Gamage Jayani Lakshika, Piyadi Gamage Jayani Lakshika, Dianne Cook (Monash University), Paul Harrison (Monash University), Michael Lydeamore (Dr), Thiyanga S. Talagala (University of Sri Jayewardenepura, Sri Lanka)

high-dimensional data vizualization non-linear dimension reduction tour

SPEED 3: Statistical Methods for High Dimensional and Complex Data , Part 1

20 Presentations