- Sahu, S. K. (2023) Introduction to Probability, Statistics & R, Foundations for Data-Based Sciences (1st ed.). Springer, ISBN: 978-3-031-378644-5.
- Sahu, S. K. (2022) Bayesian Modeling of Spatio-Temporal Data with R (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9780429318443
- Pierfrancesco Alaimo Di Loro, Xiang Qian, Sahu, S. K., Dankmar Boehning (2023) A novel Bayesian spatio-temporal model for the disease infection rate of Covid-19 cases in England. Technical Report, University of Southampton.
- Utazi, C. E., Jochem, W. C., Gacic-Dobo, Marta, Murphy, P., Sahu, S. K., Danovaro-Holliday, M. C., Tatem, A. J. (2023) Bayesian hierarchical modelling approaches for combining information from multiple data sources to produce annual estimates of national immunization coverage. Technical Report, University of Southampton.
- Utazi CE, Aheto JMK, Chan HMT, Tatem AJ, Sahu, S. K.. (2022) Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines.
*Statistics in Medicine.*2022 Dec 20;41(29):5662-5678. doi: 10.1002/sim.9586. Epub 2022 Sep 21. PMID: 36129171. - Sahu, S. K. and Boehning, D. (2021)
Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England.
*Spatial Statistics.*doi: 10.1016/j.spasta.2021.100519. - Hammond, M.L., Beaulieu, C., Henson, S.A. and Sahu, S. K. (2020)
Regional surface chlorophyll trends and uncertainties in the global ocean.
*Scientific Reports,***10,**15273 (2020). doi:10.1038/s41598-020-72073-9 - Sambasivan, R., Das, S. and Sahu, S. K. (2020) A Bayesian Perspective of Statistical Machine Learning for Big Data.
*Computational Statistics.*https://doi.org/10.1007/s00180-020-00970-8. - Sahu, S. K., Bakar, K. S., Zhan, J., Campbell, J. L. and Yanai, R. D. (2020)
Spatio-temporal Bayesian modeling of precipitation using rain gauge data from the Hubbard Brook Experimental Forest, New Hampshire, USA.
*Proceedings of the Joint Statistical Meetings*American Statistical Association. Pages: 77-92. - Sahu, S. K., Bass, M. R., Sabariego, C., Cieza, A.,
Fellinghauer, C. S. and Chatterji, S. (2020)
A full Bayesian implementation of a generalised partial credit model with an application to an
international disability survey.
*Journal of the Royal Statistical Society,*Series C, Applied Statistics,**69,**131-150. https://doi.org/10.1111/rssc.12385 Here are the data and code files. The paper can be accessed from the journal website as well: https://rss.onlinelibrary.wiley.com/doi/10.1111/rssc.12385. - Nicolis, O. Diaz, M., Sahu, S. K. and Marin, J. C. (2019)
Bayesian spatio-temporal modelling for estimating short-term exposure to air pollution in Santiago de Chile.
*Environmetrics,***30,**doi: 10.1002/env.2574 - Bass, M. R. and Sahu, S. K. (2019)
Dynamically Updated Spatially Varying Parameterizations of Hierarchical Bayesian Models for Spatial Data.
*Journal of Computational and Graphical Statistics.***28,**105-116, doi:10.1080/10618600.2018.1482761. - Utazi, C. E., Sahu, S. K., Atkinson, P. M., Tejedor-Garavito, N., Lloyd C. T. and Tatem, A. J. (2018). Assessing the coverage of demographic surveillance systems in sub-Saharan Africa for characterising the drivers of childhood mortality.
*BMJ Global Health,*doi:10.1136/ bmjgh-2017-000611. - Hammond, M. L., Beaulieu, C., Henson, S. A. and Sahu, S. K. (2018). Assessing the effect of discontinuities in the ocean color satellite record on chlorophyll trends and their uncertainties.
*Geophysical Research Letters,*doi: 10.1029/2017GL076928.**45,**7654-7662, - Hammond, M. L., Beaulieu, C. Sahu, S. K., Henson, S. A. (2017).
Assessing trends and uncertainties in satellite-era ocean chlorophyll using space-time modeling.
*Global Biogeochemical Cycles*. doi:10.1002/2016GB005600. - Mukhopadhyay, S. and Sahu, S. K. (2017)
A Bayesian spatio-temporal model to estimate long term exposure to outdoor air pollution at coarser administrative geographies in England and Wales.
*Journal of the Royal Statistical Society,*Series A,**181**, 465-486,*doi:10.1111/rssa.12299*A webinar was hosted by the Royal Statistical Society on February 21, 2018 with Prof Richard Chandler (UCL) in the Chair and Prof Jonathan Rougier (Bristol) as the discussant.

Here is the pdf presentation file. A youtube video of the webinar is also available. Discussion starts after 20 minutes into the video.

- Bass, M. R. and Sahu, S. K. (2017)
A comparison of centering parameterisations of Gaussian process based models for Bayesian computation using MCMC.
*Statistics and Computing,***27,**1491-1512, doi 10.1007/s11222-016-9700-z. - Pannullo, F. , Lee, D., Neal, L., Dalvi, M., Agnew, P., O'Connor, F. M., Mukhopadhyay, S., Sahu, S. K. and Sarran, C. (2017) Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England.
*Environmental Health,***16,**doi:10.1186/s12940-017-0237-1) (PMID:28347336) (PMCID:PMC5368918). - Lee, D., Mukhopadhyay, S., Rushworth, A. and Sahu, S. K. (2016)
A rigorous statistical framework for estimating the long-term health impact of air pollution. Click here for supplementary materials.
*Biostatistics,*DOI: 10.1093/biostatistics/kxw048. - Utazi, C. E., Sahu, S. K., Atkinson, P. M., Tejedor, N. and Tatem, A. J. (2016)
A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks
*Spatial Statistics***16,**161-178. - Pirani, M., Panton, A., Purdie, D. A., Sahu, S. K. (2016)
Modelling macronutrient dynamics in the Hampshire Avon river: A Bayesian approach to estimate effect of storm events
*Science of the Total Environment.*http://dx.doi.org/10.1016/j.scitotenv.2016.04.129 - Sahu, S. K. and Mukhopadhyay, S. (2016) On generating a flexible class of anisotropic spatial models using Gaussian predictive processes.
- Minty, J., Harper, H., Sarran, C., Sahu, S. K., and Baffour, B. (2013). Simulating Occupancy for Short-Term Hospital Planning.
*Technical Report,*University of Southampton. - Lee, D. and Sahu, S. K. (2016) Estimating the health impact of environmental pollution fields. In
*Handbook of Spatial Epidemiology.*Editors: Lawson, A., Banerjee, S., Haining, R. and Ugarte, L. Chapman and Hall/CRC Press. - Sahu, S. K. (2015) Bayesian Spatio-Temporal Modelling to Deliver More Accurate
and Instantaneous Air Pollution Forecasts. In
*UK Success Stories in Industrial Mathematics.*Editors: P. Aston, T. Mulholland and K. Tant. Springer International. 67-74. - Bakar, K. S. and Sahu, S. K. (2015)
spTimer: Spatio-Temporal Bayesian Modelling Using R.
*Journal of Statistical Software.***63**doi: 10.18637/jss.v063.i15 - Sahu, S. K., Bakar, K. S. and Awang, N. (2015) Bayesian Forecasting Using Hierarchical Spatio-temporal
Models with Applications to Ozone Levels in the Eastern United States. In
*Geometry Driven Statistics,*Editors: I. L. Dryden and J. Kent. John Wiley and Sons. Chapter 13, pp 260-281. - Lee, D., Rushworth, A., and Sahu, S. K. (2014) A Bayesian localised conditional auto-regressive model for estimating the health effects of air pollution.
*Biometrics,***70**, 419-429. - Ewings, S. M., Sahu, S. K., Byrne, C. D., Chipperfield, A. J. (2014)
A Bayesian network for modelling blood glucose concentration and exercise in type 1 diabetes.
*Statistical Methods in Medical Research,***24,**342-372, doi: 10.1177/0962280214520732. - Sahu, S. K., Baffour, B., Harper, P. R., Minty, J. H. and
Sarran, C. (2014)
A Hierarchical Bayesian Model for Improving Short-Term Forecasting of Hospital Demand by Including Meteorological Information.
*Journal of the Royal Statistical Society,*Series A.**177,**39-61. - Ren, C., Sun, D. and Sahu, S. K.(2013)
Objective Bayesian Analysis of Spatial Models with Separable Correlation Functions.
*The Canadaian Journal of Statistics.***41,**488-507. - Sahu, S. K. and Bakar, K. S. (2012)
Hierarchical Bayesian auto-regressive models for large space time data with applications to ozone concentration modelling.
*Applied Stochastic Models in Business and Industry,***28,**395-415. - Gelfand, A. E., Sahu S. K. and Holland, D. M. (2012)
On the Effect of Preferential Sampling in Spatial Prediction.
*Environmetrics,***23,**565-578. - Sahu, S. K. (2012)
Hierarchical Bayesian models for space-time air pollution data In
*Handbook of Statistics-Vol 30. Time Series Analysis, Methods and Applications.*Editors: T Subba Rao and C R Rao. Elsevier Publishers, Holland. Elsevier Publishers, Amsterdam, pp 477-495. - Sahu, S. K. and Bakar, K. S. (2012)
A comparison of Bayesian Models for Daily Ozone Concentration Levels
*Statistical Methodology*,**9,**144-157, DOI: 10.1016/j.stamet.2011.04.009. - Sahu, S. K., Yip, S. and Holland, D. M. (2011)
A fast Bayesian method for updating and forecasting hourly ozone levels.
*Environmental and Ecological Statistics,***18,**185-207, DOI 10.1007/s10651-009-0127-y. - Sahu, S. K., Gelfand, A. E. and Holland, D. M. (2010)
Fusing point and areal level space-time
data with application to wet deposition.
*Journal of the Royal Statistical Society*, Series C, Applied Statistics,**59**, 77-103. - Gelfand, A. E. and Sahu, S. K. (2009)
Combining Monitoring Data and Computer model Output
in Assessing Environmental Exposure.
In
*Handbook of Applied Bayesian Analysis*edited by Anthony OHagan and Mike West, pp482-510. - Sahu, S. K. and Chai, H. S. (2009)
A new skew-elliptical distribution and its properties.
*Calcutta Statistical Association Bulletin,***61,**197--225. - Sahu, S. K., Yip, S. and Holland, D. M. (2009)
Improved space-time forecasting of next day ozone concentrations in the eastern U.S
*Atmospheric Environment,***43,**494-501. - Sahu, S. K. and Nicolis, O. (2008) An evaluation of European air pollution regulations for particulate matter monitored from a heterogeneous network.
*Environmetrics,***20:**943--961. DOI:10.1002/env.965. - Sahu, S. K. and Challenor, P. (2008)
A space-time model for joint modeling of ocean temperature and salinity levels as measured by Argo floats
*Environmetrics,***19:**509--528. - Sahu, S. K., Gelfand, A. E. and Holland, D. M. (2007)
High Resolution Space-Time Ozone
Modeling for Assessing Trends.
*Journal of the American Statistical Association.***102**, 1221--1234. - Jona Lasinio, G., Sahu, S. K. and Mardia, K. V. (2007)
Modeling rainfall data using a Bayesian Kriged-Kalman model.
In
*Bayesian Statistics and its Applocations*edited by S. K. Upadhya, U. Singh and D. K. Dey. Anshan Ltd. London. - Sahu, S. K., Gelfand, A. E. and Holland, D. M. (2006)
Spatio-temporal modeling of fine particulate matter.
Journal of Agricultural, Biological, and Environmental Statistics.
**11**, 61--86. - Sahu, S. K. and Smith, T. M. F. (2006)
A Bayesian method of sample size determination with practical applications
*Journal of the Royal Statistical Society*, Series A.**169**, 235--253. - Sahu S.K., Jona Lasinio G., Orasi A., and Mardia, K.V. (2005).
A Comparison
of Spatio-Temporal Bayesian Models for Reconstruction of Rainfall Fields
in a Cloud Seeding Experiment.
*Journal of Mathematics and Statistics***1**(4), pp. 273--281 ISSN: 1549-3644. - Sahu, S. K. and Mardia, K. V. (2005)
Recent Trends in Modeling Spatio-Temporal Data.
In Proceedings of the special meeting on Statistics and Environment organized
by the Societ\`{a} Italiana di Statistica held in Universit\`{a} Di Messina,
September 21-23, 2005,
**Invited Papers,**pages 69--83. Published by the Universit\`{a} Di Messina, Messina, Italy. - Sahu, S. K. and Mardia, K. V. (2005)
A Bayesian Kriged-Kalman model for short-term forecasting
of air pollution levels.
*Journal of the Royal Statistical Society, Series C, Applied Statistics,***54,**223--244. - Sahu, S. K. and Dey, D. K. (2004)
On a Bayesian multivariate survival
models with a skewed frailty
In
*Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality,*M. G. Genton (ed). CRC/Chapman & Hall, Boca Raton, FL, pp. 321-338 . - Sahu, S. K. (2004)
Applications of formal model choice to archaeological
chronology building.
In
*Tools for Constructing Chronologies: Crossing Disciplinary Boundaries,*Buck, C.E. and Millard, A. R. (eds). London: Springer-Verlag. pp 111--127. - Sahu, S. K., Dey, D. K. and Branco, M. D. (2003)
A New Class of Multivariate
Skew Distributions with Applications to Bayesian Regression Models.
*The Canadian Journal of Statistics.***31**129--150. - Sahu, S. K. and Zhigljavsky, A. A. (2003)
Self Regenerative Markov Chain Monte Carlo with
Adaptation.
*Bernoulli.***9,**395--422. - Sahu, S. K. and Cheng, R. C. H. (2003)
A Fast Distance Based Approach for Determining the
Number of Components in Mixtures
*The Canadian Journal of Statistics.***31**3--22. - Sahu, S. K. (2002)
Bayesian Estimation and Model Choice in Item Response
Models.
*Journal of Statistical Computation and Simulation.***72,**217--232. - Roberts, G. O. and Sahu, S. K. (2001)
Approximate pre-determined convergence properties of the Gibbs sampler.
*Journal of Computational and Graphical Statistics.***10,**216--229. - Buck, C. E. and Sahu, S. K. (2000)
Bayesian models for relative archaeological
chronology building.
*Applied Statistics.***49,**423--440 - Sahu, S. K. and Dey, D. K. (2000)
A Comparison of Frailty and Other Models for Bivariate Survival Data.
*Lifetime Data Analysis.***6**207--228. - Sahu, S. K. and Roberts, G. O. (1999)
On Convergence of the EM Algorithm and the Gibbs Sampler.
*Statistics and Computing.***9,**55--64. - Gelfand, A. E. and Sahu, S. K. (1999)
Identifiability, improper priors, and Gibbs sampling for generalized linear
models.
*Journal of the American Statistical Association.***94,**247--253. - Gilks, W. R., Roberts, G. O. and Sahu, S. K. (1998)
Adaptive Markov Chain Monte Carlo through Regeneration.
*Journal of the American Statistical Association,***93,**1045--1054. - Roberts, G. O. and Sahu, S. K. (1997) Updating Schemes, Correlation Structure,
Blocking and Parameterisation for the Gibbs Sampler.
*Journal of the Royal Statistical Society,*B,**59,**291--317. - Sahu, S. K., Dey, D. K., Aslanidou, H. and Sinha, D. (1997)
A Weibull Regression
Model with Gamma Frailties for Multivariate Survival Data.
*Lifetime Data Analysis,***3,**123--137. - Gelfand, A. E., Sahu, S. K. and Carlin, B. P. (1996)
Efficient parametrizations for generalized linear mixed models,
(with discussion).
In
*Bayesian Statistics 5*, J.M. Bernardo, J.O. Berger, A.P. Dawid and A.F.M. Smith, Oxford: Oxford University Press, pp. 165--180. - Gelfand, A. E., Sahu, S. K. and Carlin, B. P. (1995)
Efficient parametrizations for normal linear mixed models.
*Biometrika,***82,**479--488. - Dey, D. K., Kuo, L. and Sahu, S. K. (1995)
A Bayesian Predictive Approach to Determining the Number of
Components in a Mixture Distribution
*Statistics and Computing,***5**, 297--305. - Gelfand, A. E. and Sahu, S. K. (1994)
On Markov Chain Monte Carlo Acceleration.
*Journal of Computational and Graphical Statistics***3,**261--276. - Sahu, S. K., Bendel, R. B. and Sison, C. P. (1993)
Effect of Relative Risk and Cluster Configuration on the Power of the
One-dimensional Scan Statistic.
*Statistics in Medicine,***12,**1853--1865. - Mukhopadhyay, N., Chattopadhyay, S. and Sahu, S. K. (1993)
Further Developments in Estimation of the Largest Mean of
*K*Normal Populations.*Metrika,***40,**173--183.

- Sahu, S. K. (2009)
Comment on the paper "A Moving Average Approach for Spatial Statistics Models of Stream Networks", by J. M. Ver Hoef and E. E. Peterson.
*Journal of the American Statistical Association*,**105,**21-22. - Sahu, S. K. (2009) Comments on "Approximate Bayesian Inference for latent
Gaussian models using integrated nested Laplace Approximations" by Rue,
Martino and Chopin.
*Journal of the Royal Statistical Society,*B.**71,**358--359. - Sahu, S. K. (2009) Report on the spatial statistics
meeting held in Southampton on June 19, 2009.
*RSS News,***37,**Number 2, pp 9. - Gelfand, A. E. and Sahu, S. K. (2005) Comments
on ``On Model Expansion, Model Contraction, Identifiability and
Prior Information: Two Illustrative scenerios Involving
Mis-measured Variables" by Paul Gustafson.
*Statistical Science.***20,**130-131. - Sahu, S. K. and Mardia, K. V. (2004) Comments on the paper
"A conditional approachfor multivariatextreme values" by
Heffernan, J. E. and Tawn, J. A.
*Journal of the Royal Statistical Society,*B,**66,**536. - Sahu, S. K. (2002) Comments on the paper "Bayesian measures of model complexity and fit"
by Spiegelhalter, D., Best, Carlin and van der Linde.
*Journal of the Royal Statistical Society,*B,**64**625--626. - Sahu, S. K. (2002) Review of the book Analysis of Multivariate Survival Data by P. Hougaard.
*Biometrics***58**259. - Sahu, S. K. (2001) Review of the book
*Monte Carlo Methods in Bayesian Computation*by Chen, M.-H., Shao, Q.-M. and Ibrahim, J. G.*Biometrics***57,**326. - Sahu, S. K. (2000) Comments on the paper "Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives" by Durbin, J and Koopman, S. J.
*Journal of the Royal Statistical Society,*B,**62,**35--36. - Roberts, G. O. and Sahu, S. K. (1997)
Discussion of the paper "The EM Algorithm--An Old Folk-Song Sung to a Fast New Tune" by Meng, X.-L. and van Dyk, D.
*Journal of the Royal Statistical Society,*B,**59**, 558--559. - Sahu, S. K. and Gelfand A. E. (1996) Comment on
``Convergence of Markov Chain Monte Carlo Algorithms"
In
*Bayesian Statistics 5*, J.M. Bernardo, J.O. Berger, A.P. Dawid and A.F.M. Smith, Oxford: Oxford University Press, pp. 316--317. - Roberts, G. O., Sahu, S. K. and Gilks, W. R. (1995)
Comment on ``Bayesian Computation and Stochastic Systems".
*Statistical Science,***10,**49--51.