Associate Professor Robert Clark

Robert Clark

RSFAS

Research School of Finance, Actuarial Studies & Statistics

Position
Associate Professor
Email
Robert.Clark@anu.edu.au
Office
Room 4.35, CBE Bld (26C)
Link
https://researchportalplus.anu.edu.au/en/persons/robert-clark
Research areas

Statistics; Sample surveys; Statistics in ecology; Analysis of informatively selected data; Sample design.

Biography

Robert Clark is an Associate Professor in Statistics, an Accredited Statistician, and an Elected Member of the International Statistical Institute. Robert’s research interests broadly span the analysis of informatively selected samples, design of sample surveys and observational studies, small area estimation, analysis of binary data, and sampling subpopulations. He has extensive experience in sample surveys, statistical ecology, and applied statistics, both in research and in significant consulting projects. Robert’s work has attracted substantial research funding, including through an Australian Research Council Linkage Project, a Statistics New Zealand official statistics research grant, and research projects for a variety of Australian and New Zealand organisations. His research has been published in leading scholarly journals including the Journal of the Royal Statistical Society Series A and C, Statistics in Medicine, New Phytologist and the International Statistical Review. Robert also authored a major textbook on model-based survey sampling and a number of book chapters.  He has been an invited speaker at conferences held by the Australian, Canadian and American Statistical Associations, and has delivered the Ken Foreman lecture for the Statistical Society of Australia. Robert has also been an Associate Editor for a number of journals including the Journal of the Royal Statistical Society, Series A.

View ORCID profile

Research publications

Books and Book Chapters

Clark, R.G. and Templeton, R.T. (2014). Sampling the Māori Population using Proxy Screening, the Electoral Roll and Disproportionate Sampling in the New Zealand Health Survey. Chapter 22 of Hard to Survey Populations, Cambridge University Press: Cambridge.

Chambers, R.L. and Clark, R.G. (2012). An Introduction to Model-Based Survey Sampling. Oxford University Press: Oxford.

Peer Reviewed Articles

  1. Clark, R.G. (2020), Maximum likelihood estimation for outcome‐dependent samples. Aust. N. Z. J. Stat., 62: 49-70. doi:10.1111/anzs.12287.
  2. Youngentob, K., Au, J., Clark, R.G., Allen, C., Marsh, K. and Foley, W. (2019). A nutritional mechanism underpinning folivore occurrence in disturbed forests. Forestry Ecology and Management. 453: 117585. https://doi.org/10.1016/j.foreco.2019.117585. 
  3. Marsh, K., Wallis, I., Kulheim, C., Clark, R.G., Nicolle, D., Foley, W. and Salminen, J. (2019). New approaches to tannin analysis of leaves can be used to explain in vitro biological activities associated with herbivore defence. New Phytologist. https://doi.org/10.1111/nph.16117. 
  4. Clark, R.G. (2019). Model-assisted sample design is minimax for model-based prediction. Survey Methodology. Forthcoming (accepted 25/3/19).
  5. Murray, C., Clark, R.G., Mendolia, S. and Siminski, P. (2018). Direct Measures of Intergenerational Income Mobility for Australia. Economic Record 94(307), pp.445-468.
  6. Vette, K., Bareja, C., Clark, R. and Lal, A. (2018). Establishing severity indicators and thresholds for Australian influenza surveillance using the World Health Organization’s Pandemic Influenza Severity Assessment and the Moving Epidemic Method. Bulletin of the World Health Organization, 96, pp.558-567.
  7. Clark, R.G. and Barr, M. (2018). A blended link approach to relative risk regression. Statistical Methods in Medical Research 27(11), pp.3325-3339.
  8. Au, J., Youngentob, K.N., Clark, R.G., Phillips, R. and Foley, W.J. (2017). Bark chewing reveals a nutrient limitation of leaves for a specialist folivore. Journal of Mammalogy, 98(4), pp.1185-1192.
  9. Clark, R.G., Kokic, P. and Smith, P.A. (2017). A comparison of two robust estimation methods for business surveys. International Statistical Review, 85(2), pp.270-289.
  10. Clark, R.G. (2016). Statistical efficiency in distance sampling. PLoS One 11(3): e0149298. doi:10.1371/journal.pone.0149298.
  11. Molefe, W. and Clark, R.G. (2015). Model-assisted optimal allocation for planned domains using composite estimation. Survey Methodology, 41(2), pp. 377-387.
  12. Molefe, W., Shangodoyin, D. and Clark, R.G. (2015). An approximation to the optimal subsample allocation for small areas. Statistics in Transition, 16(2), pp. 163-182.
  13. Lago, L.P. and Clark, R.G. (2015). Imputation of household survey data using linear mixed models. Australian and New Zealand Journal of Statistics 57(2), pp.169-187.
  14. Steel, D.G. and Clark, R.G. (2014). Potential gains from using unit level cost information in a model-assisted framework. Survey Methodology, 40(2), pp.231-242.
  15. Clark, R.G., Templeton, R. and McNicholas, A. (2013). Developing the design of a continuous national health survey for New Zealand. Population Health Metrics, 11:25.
  16. Clark, R.G. (2013). Sample design using imperfect design data. Journal of Survey Statistics and Methodology, 1(1), pp.6-23.
  17. Steel, D. G. & Clark, R.G. (2011). Conditional and unconditional models in model-assisted estimation of finite population totals. Pakistan Journal of Statistics, 27 (4), 529-541.
  18. Clark, R.G. and Allingham, S. (2011). Robust resampling confidence intervals for empirical variograms. Mathematical Geosciences, 43 (2), pp.529-541.
  19. Thomas, A. O., Milham, P. J., Morrison, R. J., Clark, R. G. & Alvarez, R. (2011). Oxygen exchange during the reaction of POCl3 and water. Australian Journal of Chemistry, 64 (10), 1360-1365.
  20. Alzoubi, L., Clark, R.G. and Steel, D.G. (2010). Adaptive Inference for Multi-Stage Survey Data. Communications in Statistics: Simulation and Computation, 39 (7), 1334-1350.
  21. Clark, R.G. (2009). Sampling of subpopulations in two stage surveys. Statistics in Medicine, Vol. 28, Issue 29, pp. 3697-3717.
  22. Clark, R.G. and Chambers, R.L. (2008). Adaptive calibration for prediction of finite population totals. Survey Methodology, vol. 34, no. 2, pp. 163-172.
  23. Clark, R.G. and Strevens, T.C. (2008). Design and analysis of clustered, unmatched resource selection studies. Jnl of the Royal Statistical Society Series C, 57(5), 535-551.
  24. Steel, D.G. and Clark, R.G. (2007). Person-level and household-level regression estimation in household surveys. Survey Methodology vol. 33, pp.51-60.
  25. Clark, R.G. and Steel, D.G. (2007). Sampling within households in household surveys. Journal of the Royal Statistical Society Series A, vol. 170, part 1, pp. 63-82.
  26. Clark, R.G. and Steel, D.G. (2002). The use of households as sampling units. International Statistical Review vol. 70, no.2, pp. 289-314.
  27. Clark, R.G. and Steel, D.G. (2000). Optimal allocation to strata and stages with simple additional constraints. The Statistician vol. 49, pp.197-207.
  28. Smyth, G.K, Chakraborty, S., Clark, R.G. and Pettit, A.N. (1992). A stochastic model for anthracnose development in stylosanthes scabra. Phytopathology 82: 1267-1272.

 

Research grants and awards

Chief Investigator, Australian Research Council (ARC) Linkage grant on Handling Missing Data in Complex Household Surveys, 2007 – 2010.

Chief Investigator of a project on Sampling for Subpopulations in Household Surveys with Application to Maori and Pacific Sampling, July 2007 – June 2008. Statistics New Zealand Official Statistics Research Fund.

Investigator on “The role of households, neighbourhoods and networks in social statistics”, funded by a 2008 – 2010 International Social Sciences Collaboration linkage grant between the ARC and the UK Economic and Social Research Council.

Research engagement and outreach

Associate Editor, Australian and New Zealand Journal of Statistics

Member, Australian Bureau of Statistics Methodology Advisory Committee


Additional information

Accredited Statistician (Statistical Society of Australia)

Elected Member of the International Statistical Institute

Teaching

Master of Statistical Data Analysis Example Study Plan (commencing semester 1)

  • For full requirements and course lists, see the MSDA Programs and Courses page, particularly the study tab.
     
  • Semester 1:
    • STAT7038 (Regression Modelling)
    • STAT7039 Principles of Mathematical Statistics
    • STAT6026 Graphical Data Analysis
    • one course from the elective list e.g.,:
      • COMP6730 (Programming for Scientists); or
      • POPH8101 (you will need a permission code from the Nat Centre for Epidemiology and Population Health); or
      • STAT6046 Financial Mathematics
         
  • Semester 2:
    • STAT8130 Generalised Linear Modelling
    • STAT7004 Introduction to Stochastic Processes
    • one course from the second list on Programs and Courses, e.g.,
      • STAT6016 Introduction to Applied Bayesian Analysis; or
      • STAT6017 Big Data Statistics
    • one course from the third elective lis on Programs and Courses e.g.,
      • COMP6730 Programming for Scientists; or
      • STAT6046 Financial Mathematics

Master of Statistical Data Analysis Example Study Plan (commencing semester 2)

  • For full requirements and course lists, see the MSDA Programs and Courses page, particularly the study tab.
     
  • Semester 2:
    • STAT7038 (Regression Modelling)
    • STAT7039 Principles of Mathematical Statistics
    • STAT6026 Graphical Data Analysis
    • one course from the third elective list on Programs and Courses, e.g.
      • COMP6730 (Programming for Scientists); or
      • STAT6046 Financial Mathematics
         
  • Semester 1:
    • STAT8140 Statistical Learning
    • two courses from the first or second list list on Programs and Courses, e.g.,
      • STAT7004 Introduction to Stochastic Processes
      • STAT6016 Introduction to Applied Bayesian Analysis
      • STAT8130 Generalised Linear Modelling
    • one course from any of the three lists on Programs and Courses e.g.
      • COMP6730 Programming for Scientists; or
      • STAT6046 Financial Mathematics; or
      • POPH8101 (you will need permission code from the Nat Centre for Epidemiology and Population Health)

Master of Statistics Example Study Plan (commencing semester 1)

  • For full requirements and course lists, see the MSTAT Programs and Courses page, particularly the study tab.
     
  • Year 1, Semester 1:
    • STAT7038 (Regression Modelling)
    • STAT7039 (Principles of Mathematical Statistics)
    • STAT6026 (Graphical Data Analysis)
      one course from the third list on Programs and Courses (e.g., COMP6730, Programming for Scientists)
       
  • Year 1, Semester 2:
    • STAT8130 (Generalised Linear Modelling)
    • STAT6016 (Introduction to Applied Bayesian Analysis)
    • STAT7004 (Introduction to Stochastic Processes)
    • one course from the third list on Programs and Courses (e.g., COMP6730, Programming for Scientists)
       
  • Year 2, Semester 1:
    • STAT8140 Statistical Learning
    • STAT6027 Statistical Inference
    • two courses from the second list on Programs and Courses (e.g., STAT7018, STAT6029, STAT6042, STAT8002)
       
  • Year 2, Semester 2:
    • two courses from STAT7006, STAT7017, STAT7050, STAT8056
    • two courses from the second or third list on Programs and Courses

Master of Statistics Example Study Plan (commencing semester 2)

  • For full requirements and course lists, see the MSTAT Programs and Courses page, particularly the study tab.
     
  • Year 1, Semester 2:
    • STAT7038 (Regression Modelling)
    • STAT7039 (Principles of Mathematical Statistics)
    • STAT6026 (Graphical Data Analysis)
      one course from the third list on Programs and Courses (e.g., COMP6730, Programming for Scientists)
       
  • Year 2, Semester 1:
    • STAT8130 (Generalised Linear Modelling)
    • STAT8140 Statistical Learning
    • STAT6027 Statistical Inference
    • one course from the third list on Programs and Courses (e.g., COMP6730, Programming for Scientists)
       
  • Year 2, Semester 2:
    • STAT6016 (Introduction to Applied Bayesian Analysis)
    • STAT7004 (Introduction to Stochastic Processes)
    • two courses from the second list on Programs and Courses (e.g., STAT7006, STAT7017, STAT7050, STAT8056)
       
  • Year 3, Semester 1:
    • two courses from the second list on Programs and Courses (e.g., STAT7018, STAT6029, STAT6042, STAT8002)
    • two courses from the second or third list on Programs and Courses