M Tariq Hasan, Professor

BSc, MSc (Dhaka), MSc, PhD (MUN)

Research interests

  • Mixed model for temporal, clustered and longitudinal count, binary and skewed data 
  • Mixed model for semi continuous data
  • Mixed model for zero inflated data
  • Modelling Spatial and spatiotemporal data
  • Joint modelling
  • Mixed model for cross-classified data

Selected Publications

  • Yan, G., Ma, R. and Hasan, M. T. (2019). A joint Poisson state-space modelling approach to analysis of binomial series with random cluster sizes. International Journal of Biostatistics, Published online 2019-03-21. https://doi.org/10.1515/ijb-2018-0090.
  • Ma, R., Yan, G. and Hasan, M. T. (2018). Tweedie family of generalized linear models with distribution-free random effects for skewed longitudinal data. Statistics in Medicine, Volume 37, 3519--32. http://dx.doi.org/10.1002/sim.7841.
  • Hasan, M. T., Sneddon, G. and Ma, R. (2018). Simultaneous modelling clustered marginal counts and multi-nomial proportions with zero-inflation with application to analysis of osteoporotic fractures data. Journal of Royal Statistical Society, Series C, Volume 67, 185--200. DOI: 10.1111/rssc.12216.
  • Hasan, M. T., Sneddon, G. and Ma, R. (2017). Modeling binomial amphibian roadkill data in distance sampling while accounting for zero-inflation, serial correlation and varying cluster sizes simultaneously. Environmental and Ecological Statistics, Volume 24, 201--217.
  • Pellegrini, T. R., Hasan, M. T. and Renjun, M. (2017). Modeling of paired zero-inflated continuous data without breaking down paired designs. Journal of Applied Statistics, Volume 44 (13), 2427-2443. DOI: 10.1080/02664763.2016.1254734.
  • Yan, G., Hasan, M. T. and Ma, R. (2016). Modeling proportions and marginal counts simultaneously for clustered multinomial data with random cluster sizes. Journal of Applied Statistics. Volume 43 (6), 1074--1087. DOI:  10.1080/02664763.2015.1089223.
  • Hasan M. T., Yan, G. and Renjun, M. (2014). Analysis of periodic patterns of daily precipitation through simultaneous modeling of its serially observed occurrence and amount. Environmental and Ecological Statistics, Volume 21, 811--824.
  • Hasan, M.T., Sneddon, G. and Ma, R. (2012). Regression analysis of zero-inflated time series counts: application to air pollution related emergency room visit data. Journal of Applied Statistics, Volume 39, 467--476.
  • Hasan, M.T., Sneddon, G. and Ma, R. (2009). Pattern-mixture zero-inflated mixed models for longitudinal unbalanced count data with excessive zeros. Biometrical Journal, Volume 51, 946--960.
  • Ma, R., Hasan, M.T. and Sneddon, G. (2009). Modeling heterogeneity in clustered count data with extra zeros using compound Poisson random effect. Statistics in Medicine, Volume 28, 2356--2369.

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