Dr Hongsheng Dai

Department of Mathematical Sciences
Dr Hongsheng Dai
  • Email

  • Telephone

    +44 (0) 1206 873304

  • Location

    STEM 5.18, Colchester Campus

  • Academic support hours

    Open door policy



Dr. Dai undertook his undergraduate study in Applied Mathematics at Tianjin University from 1996 to 2000 and then he went on to do his MSc in Statistics (with Shuyuan He) at Beijing University. After he completed his MSc in 2003 he came to the UK and studied for his D.Phil. (with Peter Clifford) at University of Oxford. His main research field at Oxford was Bayesian computational statistics, in particular, exact Monte Carlo simulation. Before he fulfilled all requirements of his D.Phil. in Statistics at Oxford in December 2007 (D.Phil. formally awarded in July 2008), he had already started his first academic position, as a lecturer in statistics, at Lancaster University from Sep 2006. Then he moved to Brighton University for his first permanent position in 2009 and joined University of Essex in January 2013. He is now interested in various research areas in statistical methodology and statistical applications, including Bayesian computational statistics, artificial intelligence, mixture models, graphical models, diffusion models, queuing models, non-parametric statistics, survival analysis and longitudinal analysis. PhD application Please contact me, if you have an appropriate background in statistics, mathematics, or computer science and are interested in statistics, especially in the areas of Bayesian statistics, Monte Carlo simulation, graphical models, mixture models, diffusion models and bio-statistics.


  • BS in applied mathematics, Tianjin University (2000)

  • MS in statistics, Beijing University (2003)

  • D.Phil. in statistics, Oxford University (2007)


University of Essex

  • Reader in Statistics, Mathematical Sciences, University of Essex (1/10/2019 - present)

Research and professional activities

Research interests

Bayesian computational statistics

Open to supervise

Perfect Monte Carlo sampling

Open to supervise

Mixture models

Open to supervise

Graphical models

Open to supervise

Diffusion models

Open to supervise

Queuing models

Nonparametric statistics

Open to supervise

Survival analysis

Open to supervise

Longitudinal analysis

Open to supervise

Distributed Deep Learning

Open to supervise

Teaching and supervision

Current teaching responsibilities

  • Statistics I (MA108)

  • Statistics II (MA200)

  • Research Methods (MA902)

Previous supervision

Saeed Khalfan Ali Mohammed Aldahmani
Saeed Khalfan Ali Mohammed Aldahmani
Thesis title: High-Dimensional Linear Regression Problems Via Graphical Models
Degree subject: Bio-Statistics
Degree type: Doctor of Philosophy
Awarded date: 6/3/2017
Baba Bukar Alhaji Bukar
Baba Bukar Alhaji Bukar
Thesis title: Bayesian Analysis for Mixtures of Discrete Distributions with a Non-Parametric Component
Degree subject: Statistics
Degree type: Doctor of Philosophy
Awarded date: 24/5/2016


Journal articles (27)

Liang, W. and Dai, H., (2021). Empirical Likelihood Based on Synthetic Right Censored Data. Statistics and Probability Letters. 169, 108962-108962

Zhang, Q-Z., Dai, H., Liu, M-Q. and Wang, Y., (2019). A method for augmenting supersaturated designs. Journal of Statistical Planning and Inference. 199, 207-218

Dai, H., Pollock, M. and Roberts, G., (2019). Monte Carlo Fusion. Journal of Applied Probability. 56 (1), 174-191

Liang, W., Dai, H. and He, S., (2019). Mean Empirical Likelihood. Computational Statistics and Data Analysis. 138, 155-169

Aldahmani, S., Dai, H. and Zhang, Q-Z., (2019). Hybrid Graphical Least Square Estimation and its application in Portfolio Selection. Statistics and Its Interface. 12 (4), 631-645

Dai, H., Wang, H., Restaino, M. and Bao, Y., (2018). Linear transformation models for censored data under truncation. Journal of Statistical Planning and Inference. 193, 42-54

Dai, H. and Pan, J., (2018). Joint modelling of survival and longitudinal data with informative observation times. Scandinavian Journal of Statistics: theory and applications. 45 (3), 571-589

Sampid, M., Hasim, HM. and Dai, H., (2018). Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model. PLoS ONE. 13 (6), e0198753-e0198753

Dai, H., (2017). A new rejection sampling method without using hat function. Bernoulli. 23 (4A), 2434-2465

Alhaji, BB., Dai, H., Hayashi, Y., Vinciotti, V., Harrison, A. and Lausen, B., (2016). Bayesian analysis for mixtures of discrete distributions with a non-parametric component. Journal of Applied Statistics. 43 (8), 1369-1385

Zhang, Q., Dai, H. and Fu, B., (2016). A proportional hazards model for time-to-event data with epidemiological bias. Journal of Multivariate Analysis. 152, 224-236

Dai, H., Restaino, M. and Wang, H., (2016). A class of nonparametric bivariate survival function estimators for randomly censored and truncated data. Journal of Nonparametric Statistics. 28 (4), 736-751

Aldahmani, S. and Dai, H., (2015). Unbiased Estimation for Linear Regression When n < v. International Journal of Statistics and Probability. 4 (3)

Dai, H., (2015). Exact Simulation for Fork-Join Networks with Heterogeneous Service. International Journal of Statistics and Probability. 4 (1), 19-32

Pan, J., Bao, Y., Dai, H. and Fang, H-B., (2014). Joint longitudinal and survival-cure models in tumour xenograft experiments. Statistics in Medicine. 33 (18), 3229-3240

He, S., Park, JH., Shen, H., Wu, Z. and Dai, H., (2014). Stochastic Systems: Modeling, Optimization, and Applications. Mathematical Problems in Engineering. 2014, 1-3

He, S., Park, JH., Shen, H., Wu, Z. and Dai, H., (2014). Editorial: Stochastic Systems: Modeling, Optimization, and Applications. Mathematical problems in engineering. 2014, 1-3

Dai, H., (2014). Exact Simulation for Diffusion Bridges: An Adaptive Approach. Journal of Applied Probability. 51 (2), 346-358

Dai, H., Pan, J. and Bao, Y., (2013). Modelling Survival Events with Longitudinal Covariates Measured with Error. Communications in Statistics - Theory and Methods. 42 (21), 3819-3837

Wang, H., Dai, H. and Fu, B., (2013). Accelerated failure time models for censored survival data under referral bias. Biostatistics. 14 (2), 313-326

Bao, Y., Dai, H., Wang, T. and Chuang, S-K., (2013). A joint modelling approach for clustered recurrent events and death events. Journal of Applied Statistics. 40 (1), 123-140

Dai, H., Bao, Y. and Bao, M., (2013). Maximum likelihood estimate for the dispersion parameter of the negative binomial distribution. Statistics & Probability Letters. 83 (1), 21-27

Dai, H. and Fu, B., (2012). A polar coordinate transformation for estimating bivariate survival functions with randomly censored and truncated data. Journal of Statistical Planning and Inference. 142 (1), 248-262

Dai, H., (2011). Exact Monte Carlo simulation for fork-join networks. Advances in Applied Probability. 43 (2), 484-503

Samuels, TL., Willers, JW., Uncles, DR., Monteiro, R., Halloran, C. and Dai, H., (2011). In vitro suppression of drug-induced methaemoglobin formation by Intralipid´┐Ż in whole human blood: observations relevant to the ?lipid sink theory?. Anaesthesia. 67 (1), 23-32

Dai, H. and Bao, Y., (2009). An inverse probability weighted estimator for the bivariate distribution function under right censoring. Statistics & Probability Letters. 79 (16), 1789-1797

Dai, H., (2008). Perfect sampling methods for random forests. Advances in Applied Probability. 40 (3), 897-917

Books (1)

Dai, H. and Wang, H., (2016). Analysis for Time-to-Event Data under Censoring and Truncation. 9780128054802

Book chapters (1)

Dai, H., A review on the exact Monte Carlo simulation. In: Bayesian Inference [Working Title]. Editors: Tang, N., . IntechOpen. 9781838803858

Conferences (2)

Chitsuphaphan, T., Yang, X. and Dai, H., (2020). Stochastic Programming for Residential Energy Management with Electric Vehicle under Photovoltaic Power Generation Uncertainty

Alhaji, BB., Dai, H., Hayashi, Y., Vinciotti, V., Harrison, A. and Lausen, B., (2016). Analysis of ChIP-seq Data Via Bayesian Finite Mixture Models with a Non-parametric Component

Reports and Papers (2)

Liang, W., Hu, J., Dai, H. and Bao, Y., (2020). Efficient Empirical Likelihood Inference for recovery rate of COVID-19 under Double-Censoring

Dai, H., Pollock, M. and Roberts, G., (2019). Bayesian Fusion

Grants and funding


Using smart technology to improve oral health for those with early stage dementia

University of Essex

BIAS: Responsible AI for Gender and Ethnic Labour Market Equality

Economic and Social Research Council


The project will improve efficiencies of the system, customer demand and control the price in real time.

Ocado Technology


Statistical Analysis of Electricity Smart Meter Installation Failures

Stonehaven Technology Limited

+44 (0) 1206 873304


STEM 5.18, Colchester Campus

Academic support hours:

Open door policy