Chris Lloyd

Professor of Statistics

Chris Lloyd joined Melbourne Business School in 2003 as a Professor of Statistics.

Upon completing his PhD at the University of Melbourne, Chris held Associate Professor and Senior Lecturer positions at the Australian Graduate School of Management and the University of Hong Kong.

Chris’s research has been published in leading academic journals, including Biometrika, Biometrics, Statistics and Computing, Journal of the American Statistical Association, Statistics in Medicine, Journal of the Royal Statistical Society and Asian Population Studies. His recent research focuses on population projections and their potential impact on the economy and social issues.

Chris teaches Data Analysis and Business Analytics on the MBA and Senior Executive MBA programs as well as Causal Analytics on the Masters of Business Analytics program.


Lloyd, C.J. Exact confidence limits after a group sequential single arm binary trial. 2021 Statistics in Medicine 38, 2389-2399.

Lloyd, C.J. Exact Confidence Limits Compatible with the result of a sequential trial. 2022 Journal of Statistical Planning and Inference 207, 171-176.

Lloyd, C.J., Chen, M. and Yip, S.F.P. Growing rich without growing old: the impact of internal migration in China. 2020 Asian Populaton Studies. 16(2), 183-200.

Lloyd, C.J., Kwok, R. and Yip, S.F.P. An analysis of future age and labour force profiles and its implications for rapidly changing economies: the Hong Kong experience. 2019 Journal of the Royal Statistical Society Series A, on-line.

Accurate and powerful p-values for adaptive designs with binary endpoints. Heritier, S., Lloyd CJ and S. Lo. 2018. Statistics in Medicine. 36, 2643–2655.

A new method of identifying target groups for pronatalist policy applied to Australia. Chen, M., Lloyd CJ and Yip. SFP. 2018. PLos One, 13.

Contemporary views of the 2x2 binomial trial. Lloyd CJ and Ripamino, E. 2017. Statistical Science 32, 600-615.

Importance accelerated Robbins-Monro recursion with applications to parametric confidence limits. Lloyd CJ and Botev. Z. 2015. Electronic Journal of Statistics 9(2), 2058-2075.

Computing Highly Accurate Confidence Limits from Discrete Data using Importance Sampling. Lloyd CJ and Li, D.2014. Statistics and Computing, 24, 663−673.

Accurate Confidence Limits for Stratified Clinical Trials. Lloyd CJ. 2014. Statistics in Medicine 32, 3415−3423.