Explanipedia

Advanced Distribution Theory for Significance in Scale Space Open

Rui Liu, Jan Hannig, J. S. Marron · 2025

Smoothing methods find signals in noisy data. A challenge for Statistical inference is the choice of smoothing parameter. SiZer addressed this challenge in one-dimension by detecting significant slopes across multiple scales, but was not a…

Multifaceted Neuroimaging Data Integration via Analysis of Subspaces Open

Andrew Ackerman, Zhengwu Zhang, Jan Hannig, Jack Prothero, J. S. Marron · 2025

Neuroimaging studies, such as the Human Connectome Project (HCP), often collect multifaceted data to study the human brain. However, these data are often analyzed in a pairwise fashion, which can hinder our understanding of how different b…

Variable screening based on Gaussian Centered L-moments Open

Hyowon An, Kai Zhang, J. S. Marron · 2025

PAIRWISE NONLINEAR DEPENDENCE ANALYSIS OF GENOMIC DATA. Open

Siqi Xiang, Wan Zhang, Siyao Liu, Katherine A. Hoadley, Charles M. Perou , et al. · 2025

In The Cancer Genome Atlas (TCGA) data set, there are many interesting nonlinear dependencies between pairs of genes that reveal important relationships and subtypes of cancer. Such genomic data analysis requires a rapid, powerful and inte…

Investigating the relationship between radiographic joint space width loss and deep learning-derived magnetic resonance imaging-based cartilage thickness loss in the medial weight-bearing region of the tibiofemoral joint Open

Mary Catherine Minnig, Liubov Arbeeva, Marc Niethammer, Daniel Nissman, Jennifer L. Lund , et al. · 2024

Multi-faceted Neuroimaging Data Integration via Analysis of Subspaces Open

Andrew Ackerman, Zhengwu Zhang, Jan Hannig, Jack Prothero, J. S. Marron · 2024

Neuroimaging studies, such as the Human Connectome Project (HCP), often collect multi-faceted and multi-block data to study the complex human brain. However, these data are often analyzed in a pairwise fashion, which can hinder our underst…

Angle-based joint and individual variation explained Open

Qing Feng, Meilei Jiang, Jan Hannig, J. S. Marron · 2024

Fast Algorithms for Large-Scale Generalized Distance Weighted Discrimination Open

Xin Yee Lam, J. S. Marron, Defeng Sun, Kim-Chuan Toh · 2024

High-dimension-low-sample size statistical analysis is important in a wide range of applications. In such situations, the highly appealing discrimination method, support vector machine, can be improved to alleviate data piling at the margi…

Interior Object Geometry via Fitted Frames Open

Stephen M. Pizer, Zhiyuan Liu, Junjie Zhao, Nicholas Tapp-Hughes, James Damon , et al. · 2024

We propose a means of computing fitted frames on the boundary and in the interior of objects and using them to provide the basis for producing geometric features from them that are not only alignment-free but most importantly can be made t…

The Poisson distribution model fits UMI-based single-cell RNA-sequencing data Open

Yue Pan, Justin T. Landis, Razia Moorad, Di Wu, J. S. Marron , et al. · 2023

Supplementary Figure S1 from A 10-Gene Classifier for Distinguishing Head and Neck Squamous Cell Carcinoma and Lung Squamous Cell Carcinoma Open

Anil Vachani, Michael Nebozhyn, Sunil Singhal, Linda Alila, Elliot Wakeam , et al. · 2023

Supplementary Figure S1 from A 10-Gene Classifier for Distinguishing Head and Neck Squamous Cell Carcinoma and Lung Squamous Cell Carcinoma

Supplementary Figure S1 from A 10-Gene Classifier for Distinguishing Head and Neck Squamous Cell Carcinoma and Lung Squamous Cell Carcinoma Open

Anil Vachani, Michael Nebozhyn, Sunil Singhal, Linda Alila, Elliot Wakeam , et al. · 2023

Supplementary Figure S1 from A 10-Gene Classifier for Distinguishing Head and Neck Squamous Cell Carcinoma and Lung Squamous Cell Carcinoma

Translating transcriptomic findings from cancer model systems to humans through joint dimension reduction Open

Brandon A. Price, J. S. Marron, Lisle E. Mose, Charles M. Perou, Joel S. Parker · 2023

Patterns of variation among baseline femoral and tibial cartilage thickness and clinical features: Data from the osteoarthritis initiative Open

Thomas H. Keefe, Mary Catherine Minnig, Liubov Arbeeva, Marc Niethammer, Zhenlin Xu , et al. · 2023

This exploratory analysis, combining the rich OAI dataset with novel methods for determining and visualizing cartilage thickness, reinforces known associations in knee OA while providing insights into the potential for data integration in …

Evidence for Multiple Subpopulations of Herpesvirus-Latently Infected Cells Open

Justin T. Landis, Ryan Tuck, Yue Pan, Carson N. Mosso, Anthony B. Eason , et al. · 2022

Latency is the defining characteristic of the Herpesviridae and central to the tumorigenesis phenotype of Kaposi’s sarcoma-associated herpesvirus (KSHV). KSHV-driven primary effusion lymphomas (PEL) rapidly develop resistance to therapy, s…

Joint and individual analysis of breast cancer histologic images and genomic covariates Open

Iain Carmichael, Benjamin C. Calhoun, Katherine A. Hoadley, Melissa A. Troester, Joseph Geradts , et al. · 2021

The two main approaches in the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genomics. While both histopathology and genomics are fundamental to cancer research, the connections between these fi…

Asymptotic optimality of the least-squares cross-validation bandwidth for kernel estimates of intensity functions Open

Maria M. Brooks, J. S. Marron · 2021

In this paper, kernel function methods are considered for estimating the intensity function of a non-homogeneous Poisson process. A least-squares cross-validation bandwidth for the kernel intensity estimator is introduced, and it is proven…

Statistical Inference for Data Integration Open

Xi Yang, Katherine A. Hoadley, Jan Hannig, J. S. Marron · 2021

In the age of big data, data integration is a critical step especially in the understanding of how diverse data types work together and work separately. Among the data integration methods, the Angle-Based Joint and Individual Variation Exp…

Visual High Dimensional Hypothesis Testing Open

Xi Yang, Jan Hannig, J. S. Marron · 2021

In exploratory data analysis of known classes of high dimensional data, a central question is how distinct are the classes? The Direction Projection Permutation (DiProPerm) hypothesis test provides an answer to this that is directly connec…

Geometric insights into support vector machine behavior using the KKT conditions Open

Iain Carmichael, J. S. Marron · 2021

The support vector machine (SVM) is a powerful and widely used classification algorithm. This paper uses the Karush-Kuhn-Tucker conditions to provide rigorous mathematical proof for new insights into the behavior of SVM. These insights pro…

diproperm: An R Package for the DiProPerm Test Open

Gwen Andrew, J. S. Marron, G. Hudgens Michael · 2021

High-dimensional low sample size (HDLSS) data sets frequently emerge in many biomedical applications.The direction-projection-permutation (DiProPerm) test is a two-sample hypothesis test for comparing two high-dimensional distributions.The…

Statistical Significance of Clustering Using Soft Thresholding Open

Hanwen Huang, Yufeng Liu, Ming Yuan, J. S. Marron · 2020

Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important underlying structure, as opposed to spurious sampling artifacts…

diproperm: An R Package for the DiProPerm Test Open

Andrew G. Allmon, J. S. Marron, Michael G. Hudgens · 2020

High-dimensional low sample size (HDLSS) data sets emerge frequently in many biomedical applications. A common task for analyzing HDLSS data is to assign data to the correct class using a classifier. Classifiers which use two labels and a …

Theory of high-dimensional outliers Open

Hyo Young Choi, J. S. Marron · 2019

This study concerns the issue of high dimensional outliers which are challenging to distinguish from inliers due to the special structure of high dimensional space. We introduce a new notion of high dimensional outliers that embraces vario…

Variable screening based on Gaussian Centered L-moments Open

Hyowon An, Kai Zhang, Hannu Oja, J. S. Marron · 2019

An important challenge in big data is identification of important variables. In this paper, we propose methods of discovering variables with non-standard univariate marginal distributions. The conventional moments-based summary statistics …

Methods for quantitative characterization of bone injury from computed-tomography images, supplemental information Open

Matthew McCormick, P.R. Hernández, Beatriz Paniagua, Jack Prothero, J. S. Marron , et al. · 2018

Supplemental information for SPIE Medical Imaging 2019 submission.

Eigenvalue Significance Testing for Genetic Association Open

Yi‐Hui Zhou, J. S. Marron, Fred A. Wright · 2017

Summary Genotype eigenvectors are widely used as covariates for control of spurious stratification in genetic association. Significance testing for the accompanying eigenvalues has typically been based on a standard Tracy–Widom limiting di…

Persistent homology analysis of brain artery trees Open

Paul Bendich, J. S. Marron, Ezra Miller, Alex Pieloch, Sean Skwerer · 2016

New representations of tree-structured data objects, using ideas from topological data analysis, enable improved statistical analyses of a population of brain artery trees. A number of representations of each data tree arise from persisten…

A General Framework for Constrained Smoothing Open

Enno Mammen, J. S. Marron, Berwin A. Turlach, M. P. Wand · 2016

There are a wide array of smoothing methods available for finding structure in data. A general framework is developed which shows that many of these can be viewed as a projection of the data, with respect to appropriate norms. The underlyi…

High dimension low sample size asymptotics of robust PCA Open

Yi-Hui Zhou, J. S. Marron · 2015

Conventional principal component analysis is highly susceptible to outliers. In particular, a sufficiently outlying single data point, can draw the leading principal component toward itself. In this paper, we study the effects of outliers …

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