Estimation and Inference for Generalized Geoadditive Models

Exploring heterogeneity and dynamics of meteorological influences on US PM2.5: A distributed learning approach with spatiotemporal varying coefficient models

Particulate matter (PM) has emerged as a primary air quality concern due to its substantial impact on human health. Many recent research works suggest that PM2.5 concentrations depend on meteorological conditions. Enhancing current pollution control strategies necessitates a more holistic comprehension of PM2.5 dynamics and the precise quantification of spatiotemporal heterogeneity in the relationship between meteorological factors and PM2.5 levels. The spatiotemporal varying coefficient model stands as a prominent spatial regression technique adept at addressing this heterogeneity. Amidst the challenges posed by the substantial scale of modern spatiotemporal datasets, we propose a pioneering distributed estimation method (DEM) founded on multivariate spline smoothing across a domain's triangulation. This DEM algorithm ensures an easily implementable, highly scalable, and communication-efficient strategy, demonstrating almost linear speedup potential. We validate the effectiveness of our proposed DEM through extensive simulation studies, demonstrating that it achieves coefficient estimations akin to those of global estimators derived from complete datasets. Applying the proposed model and method to the US daily PM2.5 and meteorological data, we investigate the influence of meteorological variables on PM2.5 concentrations, revealing both spatial and seasonal variations in this relationship.

spVC for the detection and interpretation of spatial gene expression variation

Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC's accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis.

Predicting the onset of breast cancer using mammogram imaging data with irregular boundary

With mammography being the primary breast cancer screening strategy, it is essential to make full use of the mammogram imaging data to better identify women who are at higher and lower than average risk. Our primary goal in this study is to extract mammogram-based features that augment the well-established breast cancer risk factors to improve prediction accuracy. In this article, we propose a supervised functional principal component analysis (sFPCA) over triangulations method for extracting features that are ordered by the magnitude of association with the failure time outcome. The proposed method accommodates the irregular boundary issue posed by the breast area within the mammogram imaging data with flexible bivariate splines over triangulations. We also provide an eigenvalue decomposition algorithm that is computationally efficient. Compared to the conventional unsupervised FPCA method, the proposed method results in a lower Brier Score and higher area under the ROC curve (AUC) in simulation studies. We apply our method to data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our approach not only obtains the best prediction performance comparing to unsupervised FPCA and benchmark models but also reveals important risk patterns within the mammogram images. This demonstrates the importance of utilizing additional supervised image-based features to clarify breast cancer risk.

From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas

Functional data analysis (FDA), which is a branch of statistics on modeling infinite dimensional random vectors resided in functional spaces, has become a major research area for Journal of Multivariate Analysis. We review some fundamental concepts of FDA, their origins and connections from multivariate analysis, and some of its recent developments, including multi-level functional data analysis, high-dimensional functional regression, and dependent functional data analysis. We also discuss the impact of these new methodology developments on genetics, plant science, wearable device data analysis, image data analysis, and business analytics. Two real data examples are provided to motivate our discussions.

Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis

Time-varying networks are fast emerging in a wide range of scientific and business applications. Most existing dynamic network models are limited to a single-subject and discrete-time setting. In this article, we propose a mixed-effect network model that characterizes the continuous time-varying behavior of the network at the population level, meanwhile taking into account both the individual subject variability as well as the prior module information. We develop a multistep optimization procedure for a constrained likelihood estimation and derive the associated asymptotic properties. We demonstrate the effectiveness of our method through both simulations and an application to a study of brain development in youth. Supplementary materials for this article are available online.