

See also my Google Citation Profile.
Papers
 Hammond, M. L., Beaulieu, C., Henson, S. A. and Sahu, S. K. (2017). Assessing the effect of discontinuities in the ocean color satellite record on chlorophyll trends and their uncertainties.
 Utazi, C. E., Sahu, S. K., Atkinson, P. M., Tejedor, N., Lloyd C. T. and Tatem, A. J. (2017). Assessing the coverage of demographic surveillance systems in subSaharan Africa for characterising the drivers of childhood mortality.
 Sahu, S. K., Bass, M. R., Sabariego, C., Cieza, A.,
Fellinghauer, C. S. and Chatterji, S. (2017)
Extending the inferential capability of a generalised partial credit model using Bayesian computation:
An application to an international disability survey developed by WHO and the World Bank. Here are my slides presented at the IWSM 2017.
 Bass, M. R. and Sahu, S. K. (2016)
Dynamically updated spatially varying parameterizations of hierarchical Bayesian models for spatially correlated data
 Sahu, S. K. and Mukhopadhyay, S. (2016)
On generating a flexible class of anisotropic spatial models using Gaussian predictive processes.
 Hammond, M. L., Beaulieu, C. Sahu, S. K., Henson, S. A. (2017). Assessing trends and uncertainties in satelliteera ocean chlorophyll using spacetime modeling. Global Biogeochemical Cycles. DOI: 10.1002/2016GB005600
 Mukhopadhyay, S. and Sahu, S. K. (2017)
A Bayesian spatiotemporal 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, 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.
 Lee, D., Mukhopadhyay, S., Rushworth, A. and Sahu, S. K. (2016)
A rigorous statistical framework for estimating the longterm health impact of air pollution. Click here for supplementary materials. Biostatistics, DOI: 10.1093/biostatistics/kxw048.
 Bass, M. R. and Sahu, S. K. (2016)
A comparison of centering parameterisations of Gaussian process based models for Bayesian computation using MCMC.
Statistics and Computing, DOI 10.1007/s112220169700z.
 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, 161178.
 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
 Minty, J., Harper, H., Sarran, C., Sahu, S. K., and Baffour, B. (2013). Simulating Occupancy for ShortTerm 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 SpatioTemporal 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. 6774.
 Bakar, K. S. and Sahu, S. K. (2015)
spTimer: SpatioTemporal 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 Spatiotemporal
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 260281.
 Lee, D., Rushworth, A., and Sahu, S. K. (2014) A Bayesian localised conditional autoregressive model for estimating the health effects of air pollution. Biometrics, 70 , 419429.

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, 342372,
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 ShortTerm Forecasting of Hospital Demand by Including Meteorological Information.
Journal of the Royal Statistical Society, Series A. 177, 3961.
 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,
488507.
 Sahu, S. K. and Bakar, K. S. (2012)
Hierarchical Bayesian autoregressive models for large space
time data with applications to ozone concentration modelling. Applied Stochastic Models in Business and Industry, 28, 395415.
 Gelfand, A. E., Sahu S. K. and Holland, D. M. (2012)
On the Effect of Preferential Sampling in Spatial Prediction. Environmetrics, 23, 565578.
 Sahu, S. K. (2012)
Hierarchical Bayesian models for spacetime air pollution data In
Handbook of StatisticsVol 30. Time Series Analysis,
Methods and Applications. Editors: T Subba Rao and C R Rao.
Elsevier Publishers, Holland. Elsevier Publishers, Amsterdam, pp 477495.
 Sahu, S. K. and Bakar, K. S. (2012)
A comparison of Bayesian Models for Daily Ozone Concentration Levels
Statistical Methodology , 9, 144157, 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, 185207,
DOI 10.1007/s106510090127y.
 Sahu, S. K., Gelfand, A. E. and Holland, D. M. (2010)
Fusing point and areal level spacetime
data with application to wet deposition.
Journal of the Royal Statistical Society, Series C,
Applied Statistics, 59 , 77103.
 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, pp482510.
 Sahu, S. K. and Chai, H. S. (2009)
A new skewelliptical distribution and its properties.
Calcutta Statistical Association Bulletin, 61,
197225.

Sahu, S. K., Yip, S. and Holland, D. M. (2009)
Improved spacetime forecasting of next day ozone concentrations in the eastern U.S
Atmospheric Environment, 43,
494501.

Sahu, S. K. and Nicolis, O. (2008) An evaluation of European air pollution regulations for particulate matter monitored from a heterogeneous network.
Environmetrics, 20: 943961.
DOI:10.1002/env.965.
 Sahu, S. K. and Challenor, P. (2008)
A spacetime model for joint modeling of ocean temperature and salinity levels as measured by Argo floats
Environmetrics, 19: 509528.
 Sahu, S. K., Gelfand, A. E. and Holland, D. M. (2007)
High Resolution SpaceTime Ozone
Modeling for Assessing Trends.
Journal of the American Statistical Association. 102,
12211234.
 Jona Lasinio, G., Sahu, S. K. and Mardia, K. V. (2007)
Modeling rainfall data using a Bayesian KrigedKalman 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)
Spatiotemporal modeling of fine particulate matter.
Journal of Agricultural, Biological, and Environmental Statistics.
11, 6186.
 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, 235253.
 Sahu S.K., Jona Lasinio G., Orasi A., and Mardia, K.V. (2005). A Comparison
of SpatioTemporal Bayesian Models for Reconstruction of Rainfall Fields
in a Cloud Seeding Experiment. Journal of Mathematics and Statistics
1 (4), pp. 273281 ISSN: 15493644.
 Sahu, S. K. and Mardia, K. V. (2005)
Recent Trends in Modeling SpatioTemporal 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 2123, 2005, Invited Papers, pages 6983. Published
by the Universit\`{a} Di Messina, Messina, Italy.

Sahu, S. K. and Mardia, K. V. (2005)
A Bayesian KrigedKalman model for shortterm forecasting
of air pollution levels.
Journal of the Royal Statistical
Society, Series C, Applied Statistics,
54, 223244.

Sahu, S. K. and Dey, D. K. (2004)
On a Bayesian multivariate survival
models with a skewed frailty
In SkewElliptical Distributions and Their
Applications: A Journey Beyond Normality,
M. G. Genton (ed). CRC/Chapman & Hall, Boca Raton, FL, pp. 321338 .

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: SpringerVerlag.
pp 111127.

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 129150.

Sahu, S. K. and Zhigljavsky, A. A. (2003)
Self Regenerative Markov Chain Monte Carlo with
Adaptation.
Bernoulli. 9, 395422.

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.
31 322.

Sahu, S. K. (2002)
Bayesian Estimation and Model Choice in Item Response
Models.
Journal of Statistical Computation and Simulation.
72, 217232.

Roberts, G. O. and Sahu, S. K. (2001)
Approximate predetermined convergence properties of the Gibbs sampler.
Journal of Computational and Graphical Statistics.
10, 216229.

Buck, C. E. and Sahu, S. K. (2000)
Bayesian models for relative archaeological
chronology building. Applied Statistics.
49, 423440

Sahu, S. K. and Dey, D. K. (2000)
A Comparison of Frailty and Other Models for Bivariate Survival Data.
Lifetime Data Analysis.
6 207228.

Sahu, S. K. and Roberts, G. O. (1999)
On Convergence of the EM Algorithm and the Gibbs Sampler.
Statistics and Computing. 9, 5564.

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,
247253.

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,
10451054.

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,
291317.

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, 123137.
 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. 165180.
 Gelfand, A. E., Sahu, S. K. and Carlin, B. P. (1995)
Efficient parametrizations for normal linear mixed models.
Biometrika, 82, 479488.
 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 , 297305.
 Gelfand, A. E. and Sahu, S. K. (1994)
On Markov Chain Monte Carlo Acceleration.
Journal of Computational and Graphical Statistics
3, 261276.
 Sahu, S. K., Bendel, R. B. and Sison, C. P. (1993)
Effect of Relative Risk and Cluster Configuration on the Power of the
Onedimensional Scan Statistic.
Statistics in Medicine, 12, 18531865.
 Mukhopadhyay, N., Chattopadhyay, S. and Sahu, S. K. (1993)
Further Developments in Estimation of the Largest Mean of $K$ Normal
Populations. Metrika, 40, 173183.
Discussion and Invited Comments
 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,
2122.
 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, 358359.
 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
Mismeasured Variables" by Paul Gustafson.
Statistical Science.
20, 130131.
 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 625626.
 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 nonGaussian 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,
3536.

Roberts, G. O. and Sahu, S. K. (1997)
Discussion of the paper "The EM AlgorithmAn Old FolkSong Sung to a Fast New Tune" by Meng, X.L. and van Dyk, D.
Journal of the Royal Statistical Society, B,
59, 558559.
 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. 316317.
 Roberts, G. O., Sahu, S. K. and Gilks, W. R. (1995)
Comment on ``Bayesian Computation and Stochastic Systems".
Statistical Science,
10, 4951.

