Journal of the Indian Statistical Association (Just Accepted)
Abstract: Modeling the dynamics of COVID-19 pandemic spread is a challenging and relevant problem. Established models for the epidemic spread such as compartmental epidemiological models e.g. Susceptible-Infected-Recovered (SIR) models and its variants, have been discussed extensively in the literature and utilized to forecast the growth of the pandemic across different hot-spots in the world. The standard formulations of SIR models rely upon summary-level data, which may not be able to fully capture the complete dynamics of the pandemic growth.
Scientific Reports (2022)
Abstract: Spatial pattern modelling concepts are being increasingly used in capturing disease heterogeneity. Quantification of heterogeneity in the tumor microenvironment is extremely important in pancreatic ductal adenocarcinoma (PDAC), which has been shown to co-occur with other pancreatic diseases and neoplasms with certain attributes that make visual discrimination difficult. In this paper, we propose the GaWRDenMap framework, that utilizes the concepts of geographically weighted regression (GWR) and a density function-based classification model, and apply it to a cohort of multiplex immunofluorescence images from patients belonging to six different pancreatic diseases.
Scandinavian Actuarial Journal (2021).
Abstract: In this paper we propose a statistical modeling framework that contributes to advancing methods for modeling insurance policy premium in the actuarial literature. Specification of separate frequency and severity models, accounting for territorial risk and performing accurate inference are some of the challenges actuaries face while modeling policy premium. We focus on building a methodology that builds parsimonious and interpretable models for modeling policy premium. Policy premiums are characterized to follow a semi-continuous probability distribution, featuring a non-zero probability mass at zero along with a positive continuous support.
Canadian Journal of Statistics (2021).
Abstract: We present a scalable Bayesian modeling approach for identifying brain regions that respond to a certain stimulus and use them to classify subjects. We specifically deal with multi-subject electroencephalography (EEG) data with a binary response distinguishing between alcoholic and control groups. The covariates are matrix-variate with measurements taken for each subject at different locations across multiple time points. EEG data has a complex structure with both spatial and temporal attributes to it.
Scientific Reports (2020).
Abstract: Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression.
NeuroImage: Clinical (2020).
Abstract: In cancer radiomics, textural features evaluated from image intensity-derived gray-level co-occurrence matrices (GLCMs) have been studied to evaluate gray-level spatial dependence within the regions of interest in the brain. Most of these analysis work with summary statistics (or texture-based features) constructed using the GLCM entries, and potentially overlook other structural properties in the GLCM. In our proposed Bayesian framework, we treat each GLCM as a realization of a two-dimensional stochastic functional process observed with error at discrete time points.
Scientific Reports (2020).
Abstract: Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are affected in patients with DR. However, most studies analyzed the thickness by calculating summary statistics from retinal thickness maps of the macula region. This study aims to apply a density function-based statistical framework to the thickness data obtained through OCT, and to compare the predictive power of various retinal layers to assess the severity of DR.
Invited for Statistical Analysis and Data Mining Best Paper Session at Joint Statistical Meetings 2022. JSM 2022 Program
Statistical Analysis and Data Mining: The ASA Data Science Journal (2020).
Abstract: With the advent of modern technologies, it is increasingly common to deal with data of large dimensions in various scientific fields of study. In this paper, we develop a Bayesian approach for the classification of multi-subject high-dimensional electroencephalography (EEG) data. In this EEG data, we have a matrix of covariates corresponding to each subject from either the alcoholic or control group.
Harvard Data Science Review (2020).
Abstract: With only 536 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25. The lockdown was first extended to May 3 soon after the analysis of this paper was completed, and then to May 18 while this paper was being revised. In this paper, we use a Bayesian extension of the Susceptible-Infected-Removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other less severe non-pharmaceutical interventions.
Journal of the Royal Statistical Society: Series C (Applied Statistics) (2019).
Abstract: Because of the immense technological advances, very often we encounter data in high dimensions. Any set of measurements taken at multiple time points for multiple subjects leads to data of more than two dimensions (a matrix of covariates for each subject). We present a Bayesian variable‐selection method to identify the active regions in the brain as a response to a certain stimulus.