17 citations · 40 across the 22 of their papers we have counts for
22 papers
Minimum Density Power Divergence Estimation for the Gamma Distribution with Applications to Robust Rainfall Modeling
Arnab Hazra
Statistical modeling of rainfall amounts is of considerable importance in meteorology, hydrology, and agriculture. The gamma distribution remains one of the most popular choices fo…
Estimating Causal Attribution of Anthropogenic Forcing on High-Temperature Extremes Using a Latent Gaussian Spatial Model
Ritik Roshan Giri, Arnab Hazra
Climate change has become a significant global concern due to its capacity to cause substantial disruption to daily life by increasing the frequency and intensity of extreme weathe…
Large Wave Direction Data Modeling Using Wrapped Spatial Gaussian Markov Random Fields
Arnab Hazra
Statistical modeling of dependent directional data remains relatively underexplored, particularly in high-dimensional spatial settings. Existing approaches for spatial angular data…
A Bayesian latent Gaussian conditional autoregressive copula model for analyzing spatially-varying trends in rainfall
Sayan Bhowmik, Arnab Hazra
Assessing the availability of rainfall water plays a crucial role in rainfed agriculture. Given the substantial proportion of agricultural practices in India being rainfed and cons…
Computationally Scalable Bayesian SPDE Modeling for Censored Spatial Responses
Indranil Sahoo, Suman Majumder, Arnab Hazra +2
Observations of groundwater pollutants, such as arsenic or Perfluorooctane sulfonate (PFOS), are riddled with left censoring. These measurements have impact on the health and lifes…
A semiparametric generalized exponential regression model with a principled distance-based prior
Arijit Dey, Arnab Hazra
The generalized exponential distribution is a well-known probability model in lifetime data analysis and several other research areas, including precipitation modeling. Despite hav…
Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study
Arnab Hazra, Pratik Nag, Rishikesh Yadav +1
Increasingly large and complex spatial datasets pose massive inferential challenges due to high computational and storage costs. Our study is motivated by the KAUST Competition on…
Spatial wildfire risk modeling using mixtures of tree-based multivariate Pareto distributions
Daniela Cisneros, Arnab Hazra, Raphaël Huser
Wildfires pose a severe threat to the ecosystem and economy, and risk assessment is typically based on fire danger indices such as the McArthur Forest Fire Danger Index (FFDI) used…
Estimating Changepoints in Extremal Dependence, Applied to Aviation Stock Prices During COVID-19 Pandemic
Arnab Hazra, Shiladitya Bose
The dependence in the tails of the joint distribution of two random variables is generally assessed using -measure, the limiting conditional probability of one variable being ex…
Flexible Modeling of Nonstationary Extremal Dependence using Spatially-Fused LASSO and Ridge Penalties
Xuanjie Shao, Arnab Hazra, Jordan Richards +1
Statistical modeling of a nonstationary spatial extremal dependence structure is challenging. Max-stable processes are common choices for modeling spatially-indexed block maxima, w…
Minimum Density Power Divergence Estimation for the Generalized Exponential Distribution
Arnab Hazra
Statistical modeling of rainfall data is an active research area in agro-meteorology. The most common models fitted to such datasets are exponential, gamma, log-normal, and Weibull…
A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes
Daniela Cisneros, Yan Gong, Rishikesh Yadav +2
Motivated by the Extreme Value Analysis 2021 (EVA 2021) data challenge we propose a method based on statistics and machine learning for the spatial prediction of extreme wildfire f…
Efficient Modeling of Spatial Extremes over Large Geographical Domains
Arnab Hazra, Raphaël Huser, David Bolin
Various natural phenomena exhibit spatial extremal dependence at short spatial distances. However, existing models proposed in the spatial extremes literature often assume that ext…
Latent Gaussian Models for High-Dimensional Spatial Extremes
Arnab Hazra, Raphaël Huser, Árni V. Jóhannesson
In this chapter, we show how to efficiently model high-dimensional extreme peaks-over-threshold events over space in complex non-stationary settings, using extended latent Gaussian…
Contamination mapping in Bangladesh using a multivariate spatial Bayesian model for left-censored data
Indranil Sahoo, Arnab Hazra
Arsenic (As) and other toxic elements contamination of groundwater in Bangladesh poses a major threat to millions of people on a daily basis. Understanding complex relationships be…
Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model
Arnab Hazra, Raphaël Huser
In this work, we estimate extreme sea surface temperature (SST) hotspots, i.e., high threshold exceedance regions, for the Red Sea, a vital region of high biodiversity. We analyze…
Robust statistical modeling of monthly rainfall: The minimum density power divergence approach
Arnab Hazra, Abhik Ghosh
Statistical modeling of monthly, seasonal, or annual rainfall data is an important research area in meteorology. These models play a crucial role in rainfed agriculture, where a pr…
A multivariate spatial skew-t process for joint modeling of extreme precipitation indexes
Arnab Hazra, Brian J. Reich, Ana-Maria Staicu
To study trends in extreme precipitation across US over the years 1951-2017, we consider 10 climate indexes that represent extreme precipitation, such as annual maximum of daily pr…
A semiparametric spatiotemporal Bayesian model for the bulk and extremes of the Fosberg Fire Weather Index
Arnab Hazra, Brian J. Reich, Benjamin A. Shaby +1
Large wildfires pose a major environmental concern, and precise maps of fire risk can improve disaster relief planning. Fosberg Fire Weather Index (FFWI) is often used to measure w…
Analysis of Annual Cyclone Frequencies over Bay of Bengal: Effect of 2004 Indian Ocean Tsunami
Arnab Hazra
This paper discusses the time series trend and variability of the cyclone frequencies over Bay of Bengal, particularly in order to conclude if there is any significant difference i…
A Time-varying Parameter Based Seasonally-adjusted Bayesian State-space Model for Forecasting
Arnab Hazra
In this paper, we develop a time-varying parameter based seasonally-adjusted Bayesian state-space model for non-stationary time series datasets where both the trend and seasonal co…
A Note on the Misuse of the Variance Test in Meteorological Studies
Arnab Hazra, Sourabh Bhattacharya, Sabyasachi Bhattacharya +1
The erroneous assumption "for all distributions for which the theoretical variance can be computed independently from parameters estimated by any method different from the method o…