arXiv subjects

Samprit Banerjee

Publications and source records attributed to Samprit Banerjee.

3 recordsLinked to original sources

arXiv2025

Identification of Emotionally Stressful Periods Through Tracking Changes in Statistical Features of mHealth Data

Identifying the onset of emotional stress in older patients with mood disorders and chronic pain is crucial in mental health studies. To this end, studying the associations between passively sensed variables that measure human behaviors and self-reported stress levels collected from mobile devices is emerging. Existing algorithms rely on conventional change point detection (CPD) methods due to the nonstationary nature of the data. They also require explicit modeling of the associations between variables and output only discrete time points, which can lead to misinterpretation of stress onset timings. This is problematic when distributional shifts are complex, dependencies between variables are difficult to capture, and changes occur asynchronously across series with weak signals. In this study, we propose an algorithm that detects hotspots, defined as collections of time intervals during which statistical features of passive sensing variables and stress indicators shift, highlighting periods that require investigation. We first extend the moving sum (MOSUM) scheme to detect simultaneous changes both within and across series, and then define hotspots in two ways: using distance-based test statistics and confidence intervals. The proposed method tracks local changes in combined distributional features, enabling it to capture all types of simultaneous and asynchronous change. It does not require a specific functional relationship between series, and the results are expressed as intervals rather than as individual time points. We conduct simulations under varying signal strengths with mixed and asynchronous distributional shifts, where the proposed method outperforms benchmarks. Results on hotspot identification indicate that the two definitions are complementary. We further apply our method to ALACRITY Phase I data, analyzing hotspots from patients' stress levels and activity measures.

stat.ME↗

arXiv2025

A co-segmentation algorithm to predict emotional stress from passively sensed mHealth data

We develop a data-driven co-segmentation algorithm of passively sensed and self-reported active variables collected through smartphones to identify emotionally stressful states in middle-aged and older patients with mood disorders undergoing therapy, some of whom also have chronic pain. Our method leverages the association between the different types of time series. These data are typically non-stationary, with meaningful associations often occurring only over short time windows. Traditional machine learning (ML) methods, when applied globally on the entire time series, often fail to capture these time-varying local patterns. Our approach first segments the passive sensing variables by detecting their change points, then examines segment-specific associations with the active variable to identify co-segmented periods that exhibit distinct relationships between stress and passively sensed measures. We then use these periods to predict future emotional stress states using standard ML methods. By shifting the unit of analysis from individual time points to data-driven segments of time and allowing for different associations in different segments, our algorithm helps detect patterns that only exist within short-time windows. We apply our method to detect periods of stress in patient data collected during ALACRITY Phase I study. Our findings indicate that the data-driven segmentation algorithm identifies stress periods more accurately than traditional ML methods that do not incorporate segmentation.

stat.AP↗

arXiv2020

An Orthogonally Equivariant Estimator of the Covariance Matrix in High Dimensions and for Small Sample Sizes

We introduce an estimation method of covariance matrices in a high-dimensional setting, i.e., when the dimension of the matrix, , is larger than the sample size . Specifically, we propose an orthogonally equivariant estimator. The eigenvectors of such estimator are the same as those of the sample covariance matrix. The eigenvalue estimates are obtained from an adjusted profile likelihood function derived by approximating the integral of the density function of the sample covariance matrix over its eigenvectors, which is a challenging problem in its own right. Exact solutions to the approximate likelihood equations are obtained and employed to construct estimates that involve a tuning parameter. Bootstrap and cross-validation based algorithms are proposed to choose this tuning parameter under various loss functions. Finally, comparisons with two well-known orthogonally equivariant estimators of the covariance matrix are given, which are based on Monte-Carlo risk estimates for simulated data and misclassification errors in real data analyses. In addition, Monte-Carlo risk estimates are also provided to compare our estimates of eigenvalues to those of a consistent estimator of population eigenvalues.

math.ST↗