Whereas X-13ARIMA-SEATS (X-13) is widely used around the world to seasonally adjust economic time series, its continued longevity is jeopardized by the ongoing difficulty of maintaining its FORTRAN...

Marginal structural models (MSMs) are recognized as useful methods for addressing the issue of time-varying confounding in longitudinal studies. In the analyses of longitudinal data with binary out...

Stochastic volatility (SV) models effectively capture the time-varying variance in financial time series, and regime-switching SV models further enhance flexibility by adapting to changing market c...

Functional connectivity, the study of coordination between distinct brain regions, is a key focus in neuroscience. The Psychophysiological Interaction (PPI) model, commonly used to infer task-depen...

The influence and impact of social media campaigns on democratic elections is a critical area of research in modern big-data analytics. While the efficacy of using social media data for forecasting...

Conducting routine seasonal adjustments of economic data has been an important responsibility of federal statistical agencies for decades. Since those adjustments typically include regular checks f...

The Census Bureau Index of Economic Activity is constructed from 15 of the Census Bureau’s primary monthly economic indicators to provide a single time series reflecting the variation over time in ...

The influence and impact of social media campaigns on democratic elections is a critical area of research in modern big-data analytics. While the efficacy of using social media data for forecasting...

The influence and impact of social media campaigns on democratic elections is a critical area of research in modern big-data analytics. While the efficacy of using social media data for forecasting...

We propose a Bayesian covariate-dependent anti-logistic circadian model for analyzing activity data collected via wrist-worn wearable devices. The proposed approach integrates covariates into the m...

The hybrid EEG-fNIRS (electroencephalogram - functional near-infrared spectroscopy) modality provides a comprehensive understanding of brain activity by simultaneously capturing electrical and hemo...

Explore the article collection: Machine learning and Statistical learning. Published in Statistics.

Accurate forecasts for day-ahead photovoltaic (PV) power generation are crucial to support a high PV penetration rate in the local electricity grid and to assure stability in the grid. We use state...

We use a geometric approach to jointly characterize tumor shape and intensity along the tumor contour, as captured in magnetic resonance images, in the context of glioblastoma multiforme. Key prope...

Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving anima...

We study the problem of modeling and inference for spatio-temporal count processes. Our approach uses parsimonious parameterisations of multivariate autoregressive count time series models, includi...

Recent advances in types and extant of medical imaging technologies has led to proliferation of multimodal quantitative imaging data in cancer. Quantitative medical imaging data refer to numerical ...