A number of recent studies in the economics literature have focused on the usefulness of
factor models in the context of prediction using “big data”(see Bai and Ng, 2008; Dufour and …

Matrix factorization methods, which include Factor analysis (FA) and Principal Components
Analysis (PCA), are widely used for inferring and summarizing structure in multivariate data …

Background With the explosion in the number of methods designed to analyze bulk and
single-cell RNA-seq data, there is a growing need for approaches that assess and compare …

Medical imaging studies have collected high dimensional imaging data to identify imaging
biomarkers for diagnosis, screening, and prognosis, among many others. These imaging …

The origin of factor analysis can be traced back to Spearman's (1904) seminal paper on
general intelligence. At the time, psychologists were trying to define intelligence by a single …

Abstract Spatial Functional Data (SFD) analysis is an emerging statistical framework that
combines Functional Data Analysis (FDA) and spatial dependency modeling. Unlike …

This work focuses on Generalized Linear Mixed Models that incorporate spatiotemporal
random effects structured via Factor Model (FM) with nonlinear interaction between latent …

Functional Data Analysis (FDA) has emerged as a powerful framework for datasets that
exhibit continuous variation over specified intervals. Unlike traditional methods, FDA treats …

While Bayesian methods are praised for their ability to incorporate useful prior knowledge, in
practice, convenient priors that allow for computationally cheap or tractable inference are …

In this study, we develop factor analysis to explore areal data collected in space and time.
The main goal is to incorporate the framework with nonlinear interactions to handle a spatio …