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Assessing trends and uncertainties in satellite‐era ocean chlorophyll using space‐time modeling

Global biogeochemical cycles, 2017-07, Vol.31 (7), p.1103-1117

2017

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Introduction to Probability, Statistics and R: Foundations for Data-Based Sciences

Sahu, Sujit K

1, 2024

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On the effect of preferential sampling in spatial prediction

Environmetrics (London, Ont.), 2012-11, Vol.23 (7), p.565-578

2012

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Bayesian localized conditional autoregressive model for estimating the health effects of air pollution

Biometrics, 2014-06, Vol.70 (2), p.419-429

2014

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Bayesian Modeling of Spatio-Temporal Data with R

Sahu, Sujit K.

1, 2022

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Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England

Spatial statistics, 2022-06, Vol.49, p.100519-100519, Article 100519

2022

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spTimer : Spatio-Temporal Bayesian Modeling Using R

Journal of statistical software, 2015, Vol.63 (15), p.1-32

2015

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Regional surface chlorophyll trends and uncertainties in the global ocean

Scientific reports, 2020-09, Vol.10 (1), Article 15273

2020

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A Bayesian perspective of statistical machine learning for big data

Computational statistics, 2020-09, Vol.35 (3), p.893-930

2020

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A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health

Biostatistics (Oxford, England), 2017-04, Vol.18 (2), p.370-385

2017

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Assessing the Presence of Discontinuities in the Ocean Color Satellite Record and Their Effects on Chlorophyll Trends and Their Uncertainties

Geophysical research letters, 2018-08, Vol.45 (15), p.7654-7662

2018

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Conditional probability and ratio‐based approaches for mapping the coverage of multi‐dose vaccines

Statistics in medicine, 2022-12, Vol.41 (29), p.5662-5678

2022

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Modelling macronutrient dynamics in the Hampshire Avon river: A Bayesian approach to estimate seasonal variability and total flux

The Science of the total environment, 2016-12, Vol.572, p.1449-1460

2016

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Dynamically Updated Spatially Varying Parameterizations of Hierarchical Bayesian Models for Spatial Data

Journal of computational and graphical statistics, 2019-01, Vol.28 (1), p.105-116

2019

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High-Resolution Space-Time Ozone Modeling for Assessing Trends

Journal of the American Statistical Association, 2007-12, Vol.102 (480), p.1221-1234

2007

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A comparison of centring parameterisations of Gaussian process-based models for Bayesian computation using MCMC

Statistics and computing, 2017-11, Vol.27 (6), p.1491-1512

2017

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A full Bayesian implementation of a generalized partial credit model with an application to an international disability survey

Journal of the Royal Statistical Society Series C: Applied Statistics, 2020-01, Vol.69 (1), p.131-150

2020

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Objective Bayesian analysis of spatial models with separable correlation functions

Canadian journal of statistics, 2013-09, Vol.41 (3), p.488-507

2013

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Spatio-temporal detection for dengue outbreaks in the Central Region of Malaysia using climatic drivers at mesoscale and synoptic scale

Climate risk management, 2022, Vol.36, p.100429, Article 100429

2022

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A new class of multivariate skew distributions with applications to bayesian regression models

Canadian journal of statistics, 2003-06, Vol.31 (2), p.129-150

2003

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Fusing point and areal level space-time data with application to wet deposition

Applied statistics, 2010, Vol.59 (1), p.77-103

2010

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Improved space–time forecasting of next day ozone concentrations in the eastern US

Atmospheric environment (1994), 2009, Vol.43 (3), p.494-501

2009

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A hierarchical Bayesian model for improving short-term forecasting of hospital demand by including meteorological information

Journal of the Royal Statistical Society. Series A, Statistics in society, 2014-01, Vol.177 (1), p.39-61

2014

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A Bayesian kriged Kalman model for short-term forecasting of air pollution levels

Applied statistics, 2005-01, Vol.54 (1), p.223-244

2005

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Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models

Journal of the American Statistical Association, 1999-03, Vol.94 (445), p.247-253

1999

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Assessing trends and uncertainties in satellite‐era ocean chlorophyll using space‐time modeling

Introduction to Probability, Statistics and R: Foundations for Data-Based Sciences

On the effect of preferential sampling in spatial prediction

Bayesian localized conditional autoregressive model for estimating the health effects of air pollution

Bayesian Modeling of Spatio-Temporal Data with R

Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England

spTimer : Spatio-Temporal Bayesian Modeling Using R

Regional surface chlorophyll trends and uncertainties in the global ocean

A Bayesian perspective of statistical machine learning for big data

A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health

Assessing the Presence of Discontinuities in the Ocean Color Satellite Record and Their Effects on Chlorophyll Trends and Their Uncertainties

Conditional probability and ratio‐based approaches for mapping the coverage of multi‐dose vaccines

Modelling macronutrient dynamics in the Hampshire Avon river: A Bayesian approach to estimate seasonal variability and total flux

Dynamically Updated Spatially Varying Parameterizations of Hierarchical Bayesian Models for Spatial Data

High-Resolution Space-Time Ozone Modeling for Assessing Trends

A comparison of centring parameterisations of Gaussian process-based models for Bayesian computation using MCMC

A full Bayesian implementation of a generalized partial credit model with an application to an international disability survey

Objective Bayesian analysis of spatial models with separable correlation functions

Spatio-temporal detection for dengue outbreaks in the Central Region of Malaysia using climatic drivers at mesoscale and synoptic scale

A new class of multivariate skew distributions with applications to bayesian regression models

Fusing point and areal level space-time data with application to wet deposition

Improved space–time forecasting of next day ozone concentrations in the eastern US

A hierarchical Bayesian model for improving short-term forecasting of hospital demand by including meteorological information

A Bayesian kriged Kalman model for short-term forecasting of air pollution levels

Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models

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