Prof Sujit K Sahu

          

   

See also my Google Citation Profile.

Papers


  1. Sahu, S. K., Bass, M. R., Sabariego, C., Alarcos Cieza, Alarcos, 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.
  2. Bass, M. R. and Sahu, S. K. (2016) Dynamically updated spatially varying parameterizations of hierarchical Bayesian models for spatially correlated data
  3. Sahu, S. K. and Mukhopadhyay, S. (2016) On generating a flexible class of anisotropic spatial models using Gaussian predictive processes.
  4. 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, doi:10.1111/rssa.12299
  5. 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.
  6. 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/s11222-016-9700-z.
  7. 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.
  8. 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
  9. 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.
  10. 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.
  11. 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.
  12. 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
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Gelfand, A. E., Sahu S. K. and Holland, D. M. (2012) On the Effect of Preferential Sampling in Spatial Prediction. Environmetrics, 23, 565-578.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. Sahu, S. K. and Chai, H. S. (2009) A new skew-elliptical distribution and its properties. Calcutta Statistical Association Bulletin, 61, 197--225.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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 .
  37. 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.
  38. 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.
  39. Sahu, S. K. and Zhigljavsky, A. A. (2003) Self Regenerative Markov Chain Monte Carlo with Adaptation. Bernoulli. 9, 395--422.
  40. 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.
  41. Sahu, S. K. (2002) Bayesian Estimation and Model Choice in Item Response Models. Journal of Statistical Computation and Simulation. 72, 217--232.
  42. 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.
  43. Buck, C. E. and Sahu, S. K. (2000) Bayesian models for relative archaeological chronology building. Applied Statistics. 49, 423--440
  44. 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.
  45. Sahu, S. K. and Roberts, G. O. (1999) On Convergence of the EM Algorithm and the Gibbs Sampler. Statistics and Computing. 9, 55--64.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. Gelfand, A. E., Sahu, S. K. and Carlin, B. P. (1995) Efficient parametrizations for normal linear mixed models. Biometrika, 82, 479--488.
  52. 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.
  53. Gelfand, A. E. and Sahu, S. K. (1994) On Markov Chain Monte Carlo Acceleration. Journal of Computational and Graphical Statistics 3, 261--276.
  54. 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.
  55. 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.

Discussion and Invited Comments

  1. 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.
  2. 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.
  3. Sahu, S. K. (2009) Report on the spatial statistics meeting held in Southampton on June 19, 2009. RSS News, 37, Number 2, pp 9.
  4. 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.
  5. 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.
  6. 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.
  7. Sahu, S. K. (2002) Review of the book Analysis of Multivariate Survival Data by P. Hougaard. Biometrics 58 259.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Roberts, G. O., Sahu, S. K. and Gilks, W. R. (1995) Comment on ``Bayesian Computation and Stochastic Systems". Statistical Science, 10, 49--51.

Mathematical Sciences | S3RI | University of Southampton
Google