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Aritra Sengupta
Researcher at Ohio State University
Publications - 8
Citations - 302
Aritra Sengupta is an academic researcher from Ohio State University. The author has contributed to research in topics: Statistical model & Predictive inference. The author has an hindex of 6, co-authored 7 publications receiving 255 citations.
Papers
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Journal ArticleDOI
Modelling dendritic ecological networks in space: an integrated network perspective
Erin E. Peterson,Jay M. Ver Hoef,Daniel J. Isaak,Jeffrey A. Falke,Marie-Jos ee Fortin,Chris E. Jordan,Kristina M. McNyset,Pascal Monestiez,Aaron S. Ruesch,Aritra Sengupta,Nicholas A. Som,E. Ashley Steel,David M. Theobald,Christian E. Torgersen,Seth J. Wenger +14 more
TL;DR: In this paper, a taxonomy of network analysis methods that account for dendritic network characteristics to varying degrees is presented, and a synthesis of the different approaches within the context of stream ecology is provided.
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Hierarchical statistical modeling of big spatial datasets using the exponential family of distributions
TL;DR: This article develops maximum likelihood (ML) estimates of the unknown parameters using Laplace approximations in an expectation–maximization (EM) algorithm and applies this methodology to analyze a remote sensing dataset of aerosol optical depth.
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Predictive inference for big, spatial, non-Gaussian data: MODIS cloud data and its change-of-support
TL;DR: In this article, a hierarchical spatial-statistical modeling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud-mask data, and a spatial change-of-Support relationship to estimate cloud fraction at coarser resolutions.
Journal ArticleDOI
Empirical Hierarchical Modelling for Count Data using the Spatial Random Effects Model
Aritra Sengupta,Noel A Cressie +1 more
TL;DR: In this article, a hierarchical statistical model made up of a Poisson model for the counts and an underlying Spatial Random Effects process for the logarithm of the mean of the Poisson distribution is considered.
Statistical modeling of MODIS cloud data using the spatial random effects model
TL;DR: In this paper, a hierarchical statistical model for analyzing the cloud data is proposed, where a hidden process for the probability of clear sky is assumed to make use of the spatial random effects (SRE) model.