27 citations · 107 across the 25 of their papers we have counts for
29 papers
fastfrechet: An R package for fast implementation of Fréchet regression with distributional responses
Alexander Coulter, Rebecca Lee, Irina Gaynanova
Distribution-as-response regression problems are gaining wider attention, especially within biomedical settings where observation-rich patient specific data sets are available, suc…
A Sparse Linear Model for Positive Definite Estimation of Covariance Matrices
Rakheon Kim, Irina Gaynanova
Sparse covariance matrices play crucial roles by encoding the interdependencies between variables in numerous fields such as genetics and neuroscience. Despite substantial studies…
Beyond fixed thresholds: optimizing summaries of wearable device data via piecewise linearization of quantile functions
Junyoung Park, Neo Kok, Irina Gaynanova
Wearable devices, such as actigraphy monitors and continuous glucose monitors (CGMs), capture high-frequency data, which are often summarized by the percentages of time spent withi…
Learning Joint and Individual Structure in Network Data with Covariates
Carson James, Dongbang Yuan, Irina Gaynanova +1
Datasets consisting of a network and covariates associated with its vertices have become ubiquitous. One problem pertaining to this type of data is to identify information unique t…
Bayesian segmented Gaussian copula factor model for single-cell sequencing data
Junsouk Choi, Hee Cheol Chung, Irina Gaynanova +1
Single-cell sequencing technologies have significantly advanced molecular and cellular biology, offering unprecedented insights into cellular heterogeneity by allowing for the meas…
singR: An R package for Simultaneous non-Gaussian Component Analysis for data integration
Liangkang Wang, Irina Gaynanova, Benjamin Risk
This paper introduces an R package that implements Simultaneous non-Gaussian Component Analysis for data integration. SING uses a non-Gaussian measure of information to extract fea…
Sparse semiparametric discriminant analysis for high-dimensional zero-inflated data
Hee Cheol Chung, Yang Ni, Irina Gaynanova
Sequencing-based technologies provide an abundance of high-dimensional biological datasets with skewed and zero-inflated measurements. Classification of such data with linear discr…
Exponential canonical correlation analysis with orthogonal variation
Dongbang Yuan, Yunfeng Zhang, Shuai Guo +2
Canonical correlation analysis (CCA) is a standard tool for studying associations between two data sources; however, it is not designed for data with count or proportion measuremen…
Hierarchical nuclear norm penalization for multi-view data
Sangyoon Yi, Raymond K. W. Wong, Irina Gaynanova
The prevalence of data collected on the same set of samples from multiple sources (i.e., multi-view data) has prompted significant development of data integration methods based on…
latentcor: An R Package for estimating latent correlations from mixed data types
Mingze Huang, Christian L. Müller, Irina Gaynanova
We present `latentcor`, an R package for correlation estimation from data with mixed variable types. Mixed variables types, including continuous, binary, ordinal, zero-inflated, or…
Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks
Hee Cheol Chung, Irina Gaynanova, Yang Ni
Microorganisms play a critical role in host health. The advancement of high-throughput sequencing technology provides opportunities for a deeper understanding of microbial interact…
Double-matched matrix decomposition for multi-view data
Dongbang Yuan, Irina Gaynanova
We consider the problem of extracting joint and individual signals from multi-view data, that is data collected from different sources on matched samples. While existing methods fo…
Fast computation of latent correlations
Grace Yoon, Christian L. Müller, Irina Gaynanova
Latent Gaussian copula models provide a powerful means to perform multi-view data integration since these models can seamlessly express dependencies between mixed variable types (b…
Compressing Large Sample Data for Discriminant Analysis
Alexander F. Lapanowski, Irina Gaynanova
Large-sample data became prevalent as data acquisition became cheaper and easier. While a large sample size has theoretical advantages for many statistical methods, it presents com…
Simultaneous Non-Gaussian Component Analysis (SING) for Data Integration in Neuroimaging
Benjamin Risk, Irina Gaynanova
As advances in technology allow the acquisition of complementary information, it is increasingly common for scientific studies to collect multiple datasets. Large-scale neuroimagin…
Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring
Alexander F. Lapanowski, Irina Gaynanova
We consider the two-group classification problem and propose a kernel classifier based on the optimal scoring framework. Unlike previous approaches, we provide theoretical guarante…
Joint association and classification analysis of multi-view data
Yunfeng Zhang, Irina Gaynanova
Multi-view data, that is matched sets of measurements on the same subjects, have become increasingly common with advances in multi-omics technology. Often, it is of interest to fin…
Prediction and estimation consistency of sparse multi-class penalized optimal scoring
Irina Gaynanova
Sparse linear discriminant analysis via penalized optimal scoring is a successful tool for classification in high-dimensional settings. While the variable selection consistency of…
Sparse semiparametric canonical correlation analysis for data of mixed types
Grace Yoon, Raymond J. Carroll, Irina Gaynanova
Canonical correlation analysis investigates linear relationships between two sets of variables, but often works poorly on modern data sets due to high-dimensionality and mixed data…
Prediction Error Bounds for Linear Regression With the TREX
Jacob Bien, Irina Gaynanova, Johannes Lederer +1
The TREX is a recently introduced approach to sparse linear regression. In contrast to most well-known approaches to penalized regression, the TREX can be formulated without the us…
Sparse quadratic classification rules via linear dimension reduction
Irina Gaynanova, Tianying Wang
We consider the problem of high-dimensional classification between the two groups with unequal covariance matrices. Rather than estimating the full quadratic discriminant rule, we…
Structural Learning and Integrative Decomposition of Multi-View Data
Irina Gaynanova, Gen Li
The increased availability of the multi-view data (data on the same samples from multiple sources) has led to strong interest in models based on low-rank matrix factorizations. The…
A General Framework for Association Analysis of Heterogeneous Data
Gen Li, Irina Gaynanova
Multivariate association analysis is of primary interest in many applications. Despite the prevalence of high-dimensional and non-Gaussian data (such as count-valued or binary), mo…
Oracle Inequalities for High-dimensional Prediction
Johannes Lederer, Lu Yu, Irina Gaynanova
The abundance of high-dimensional data in the modern sciences has generated tremendous interest in penalized estimators such as the lasso, scaled lasso, square-root lasso, elastic…