17 citations · 40 across the 22 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…