arXiv subjects

Stella Self

Publications and source records attributed to Stella Self.

4 recordsLinked to original sources

arXiv2026

Variable Selection in Functional Linear Quantile Regression for Identifying Associations between Daily Patterns of Physical Activity and Cognitive Function

Quantile regression is useful for characterizing the conditional distribution of a response variable and understanding heterogeneity in the covariate effects at different quantiles. The rise of high-dimensional physiological data in biomedical research through wearable and sensor devices underscores the need for effective variable selection methods for interpretable and accurate quantile regression, which can offer robust insights into heterogeneous and dynamic covariate effects. We develop a flexible variable selection approach for functional linear quantile regression with multiple functional and scalar predictors. We use a smooth approximation of the quantile loss function and integrate functional principal component analysis (FPCA) with a group minimax concave penalty (MCP) to impose sparsity on the functional coefficients. A computationally efficient group descent algorithm is employed for optimization. Through numerical simulations, we demonstrate a satisfactory selection, estimation, and prediction accuracy of the proposed method across different quantiles for both dense and sparsely observed functional data. The proposed method is applied to accelerometer data from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) to identify key time-varying distributional patterns of physical activity and demographic predictors associated with cognitive function across different quantiles. Our analysis provides new insights into the complex relationship between the daily distributional patterns of physical activity and cognitive function among older adults, capturing heterogeneous associations across different quantiles.

stat.ME↗

arXiv2025

Variable Selection in Functional Linear Cox Model

Modern biomedical studies frequently collect complex, high-dimensional physiological signals using wearables and sensors along with time-to-event outcomes, making efficient variable selection methods crucial for interpretation and improving the accuracy of survival models. We propose a novel variable selection method for a functional linear Cox model with multiple functional and scalar covariates measured at baseline. We utilize a spline-based semiparametric estimation approach for the functional coefficients and a group minimax concave type penalty (MCP), which effectively integrates smoothness and sparsity into the estimation of functional coefficients. An efficient group descent algorithm is used for optimization, and an automated procedure is provided to select optimal values of the smoothing and sparsity parameters. Through simulation studies, we demonstrate the method's ability to perform accurate variable selection and estimation. The method is applied to 2003-06 cohort of the National Health and Nutrition Examination Survey (NHANES) data, identifying the key temporally varying distributional patterns of physical activity and demographic predictors related to all-cause mortality. Our analysis sheds light on the intricate association between daily distributional patterns of physical activity and all-cause mortality among older US adults.

stat.ME↗

arXiv2022

Cluster Detection Capabilities of the Average Nearest Neighbor Ratio and Ripley's K Function on Areal Data: an Empirical Assessment

Spatial clustering detection methods are widely used in many fields including epidemiology, ecology, biology, physics, and sociology. In these fields, areal data is often of interest; such data may result from spatial aggregation (e.g. the number disease cases in a county) or may be inherent attributes of the areal unit as a whole (e.g. the habitat suitability of conserved land parcel). This study aims to assess the performance of two spatial clustering detection methods on areal data: the average nearest neighbor (ANN) ratio and Ripley's K function. These methods are designed for point process data, but their ease of implementation in GIS software (e.g., in ESRI ArcGIS) and the lack of analogous methods for areal data have contributed to their use for areal data. Despite the popularity of applying these methods to areal data, little research has explored their properties in the areal data context. In this paper we conduct a simulation study to evaluate the performance of each method for areal data under various areal structures and types of spatial dependence. These studies find that traditional approach to hypothesis testing using the ANN ratio or Ripley's K function results in inflated empirical type I rates when applied to areal data. We demonstrate that this issue can be remedied for both approaches by using Monte Carlo methods which acknowledge the areal nature of the data to estimate the distribution of the test statistic under the null hypothesis. While such an approach is not currently implemented in ArcGIS, it can be easily done in R using code provided by the authors.

stat.AP↗

arXiv2022

A Generalization of Ripley's K Function for the Detection of Spatial Clustering in Areal Data

Spatial clustering detection has a variety of applications in diverse fields, including identifying infectious disease outbreaks, assessing land use patterns, pinpointing crime hotspots, and identifying clusters of neurons in brain imaging applications. While performing spatial clustering analysis on point process data is common, applications to areal data are frequently of interest. For example, researchers might wish to know if census tracts with a case of a rare medical condition or an outbreak of an infectious disease tend to cluster together spatially. Since few spatial clustering methods are designed for areal data, researchers often reduce the areal data to point process data (e.g., using the centroid of each areal unit) and apply methods designed for point process data, such as Ripley's K function or the average nearest neighbor method. However, since these methods were not designed for areal data, a number of issues can arise. For example, we show that they can result in loss of power and/or a significantly inflated type I error rate. To address these issues, we propose a generalization of Ripley's K function designed specifically to detect spatial clustering in areal data. We compare its performance to that of the traditional Ripley's K function, the average nearest neighbor method, and the spatial scan statistic with an extensive simulation study. We then evaluate the real world performance of the method by using it to detect spatial clustering in land parcels containing conservation easements and US counties with high pediatric overweight/obesity rates.

stat.ME↗