18 citations · 24 across the 7 of their papers we have counts for
8 papers
Generalized Matrix Decomposition Regression: Estimation and Inference for Two-way Structured Data
Yue Wang, Ali Shojaie, Timothy W. Randolph +2
This paper studies high-dimensional regression with two-way structured data. To estimate the high-dimensional coefficient vector, we propose the generalized matrix decomposition re…
A Sparsity Inducing Nuclear-Norm Estimator (SpINNEr) for Matrix-Variate Regression in Brain Connectivity Analysis
Damian Brzyski, Xixi Hu, Joaquin Goni +3
Classical scalar-response regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. In medical applications, however, regresso…
Incorporating Deep Features in the Analysis of Tissue Microarray Images
Donghui Yan, Timothy W. Randolph, Jian Zou +1
Tissue microarray (TMA) images have been used increasingly often in cancer studies and the validation of biomarkers. TACOMA---a cutting-edge automatic scoring algorithm for TMA ima…
Kernel-Penalized Regression for Analysis of Microbiome Data
Timothy W. Randolph, Sen Zhao, Wade Copeland +2
The analysis of human microbiome data is often based on dimension-reduced graphical displays and clustering derived from vectors of microbial abundances in each sample. Common to t…
Longitudinal Functional Models with Structured Penalties
Madan G. Kundu, Jaroslaw Harezlak, Timothy W. Randolph
This paper addresses estimation in a longitudinal regression model for association between a scalar outcome and a set of longitudinally-collected functional covariates or predictor…
Analyzing LC-MS/MS data by spectral count and ion abundance: two case studies
Thomas I. Milac, Timothy W. Randolph, Pei Wang
In comparative proteomics studies, LC-MS/MS data is generally quantified using one or both of two measures: the spectral count, derived from the identification of MS/MS spectra, or…
Structured penalties for functional linear models---partially empirical eigenvectors for regression
Timothy W. Randolph, Jaroslaw Harezlak, Ziding Feng
One of the challenges with functional data is incorporating spatial structure, or local correlation, into the analysis. This structure is inherent in the output from an increasing…
Statistical methods for tissue array images - algorithmic scoring and co-training
Donghui Yan, Pei Wang, Michael Linden +2
Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of d…