5 citations · 5 across the 2 of their papers we have counts for
7 papers · 1 filter
Optimal Bayesian Smoothing of Functional Observations over a Large Graph
Arkaprava Roy, Shubhashis Ghosal
In modern contexts, some types of data are observed in high-resolution, essentially continuously in time. Such data units are best described as taking values in a space of function…
Time-varying auto-regressive models for count time-series
Arkaprava Roy, Sayar Karmakar
Count-valued time series data are routinely collected in many application areas. We are particularly motivated to study the count time series of daily new cases, arising from COVID…
Analyzing initial stage of COVID-19 transmission through Bayesian time-varying model
Arkaprava Roy, Sayar Karmakar
Recent outbreak of the novel coronavirus COVID-19 has affected all of our lives in one way or the other. While medical researchers are working hard to find a cure and doctors/nurse…
Perturbed factor analysis: Accounting for group differences in exposure profiles
Arkaprava Roy, Isaac Lavine, Amy H. Herring +1
In this article, we investigate group differences in phthalate exposure profiles using NHANES data. Phthalates are a family of industrial chemicals used in plastics and as solvents…
Bayesian time-aligned factor analysis of paired multivariate time series
Arkaprava Roy, Jana Schaich-Borg, David B Dunson
Many modern data sets require inference methods that can estimate the shared and individual-specific components of variability in collections of matrices that change over time. Pro…
Nonparametric graphical model for counts
Arkaprava Roy, David B Dunson
Although multivariate count data are routinely collected in many application areas, there is surprisingly little work developing flexible models for characterizing their dependence…