Principal component analysis for big data
Big data is transforming our world, revolutionizing operations and analytics everywhere,
from financial engineering to biomedical sciences. The complexity of big data often makes …
from financial engineering to biomedical sciences. The complexity of big data often makes …
Covariate-assisted ranking and screening for large-scale two-sample inference
Two-sample multiple testing has a wide range of applications. The conventional practice first
reduces the original observations to a vector of p-values and then chooses a cut-off to adjust …
reduces the original observations to a vector of p-values and then chooses a cut-off to adjust …
[HTML][HTML] Robust high dimensional factor models with applications to statistical machine learning
Factor models are a class of powerful statistical models that have been widely used to deal
with dependent measurements that arise frequently from various applications from genomics …
with dependent measurements that arise frequently from various applications from genomics …
Robust modifications of U-statistics and applications to covariance estimation problems
Let Y be a d-dimensional random vector with unknown mean μ and covariance matrix Σ.
This paper is motivated by the problem of designing an estimator of Σ that admits …
This paper is motivated by the problem of designing an estimator of Σ that admits …
A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models
Measuring conditional dependence is an important topic in econometrics with broad
applications including graphical models. Under a factor model setting, a new conditional …
applications including graphical models. Under a factor model setting, a new conditional …
A projection based conditional dependence measure with applications to high-dimensional undirected graphical models
Measuring conditional dependence is an important topic in statistics with broad applications
including graphical models. Under a factor model setting, a new conditional dependence …
including graphical models. Under a factor model setting, a new conditional dependence …
Some permutation symmetric multiple hypotheses testing rules under dependent setup
A Kundu, SK Bhandari - South African Statistical Journal, 2023 - journals.co.za
The problem of multiple hypothesis testing with correlated test statistics is a very important
problem in statistical literature. Specifically, we consider the case when the joint distribution …
problem in statistical literature. Specifically, we consider the case when the joint distribution …
Statistical Learning Methods for Diffusion Magnetic Resonance Imaging
X Wang - 2021 - search.proquest.com
Abstract Diffusion Magnetic Resonance Imaging (dMRI) is a commonly used imaging
technique to reveal white matter (WM) microstructure by probing the diffusion of water …
technique to reveal white matter (WM) microstructure by probing the diffusion of water …
Statistical Learning of Proteomics Data and Global Testing for Data with Correlations
D Chen - 2019 - search.proquest.com
This dissertation consists of two parts. The first part is a collaborative project with Dr.
Szymanski's group in Agronomy at Purdue, to predict protein complex assemblies and …
Szymanski's group in Agronomy at Purdue, to predict protein complex assemblies and …
Large Scale Multiple Testing for High-Dimensional Nonparanormal Data
Y Xu - 2019 - scholarshare.temple.edu
False discovery control in high dimensional multiple testing has been frequently
encountered in many scientific research. Under the multivariate normal distribution …
encountered in many scientific research. Under the multivariate normal distribution …