PhD title: Methods for maximising the value of administrative and big data to produce statistics
Lyndon has nearly 20 years’ experience as an applied survey statistician at the Australian Bureau of Statistics (ABS) and has an excellent sense of the needs and pressures facing a statistical organisation. In recent years, he has driven initiatives at the ABS to develop new methods for using administrative data to produce more efficient statistics.
The increasing availability of large datasets promises to provide significant benefits for policy and decision making, however large datasets can contain inherent flaws. Through his PhD research, Lyndon aims to improve the way we harness and use externally sourced datasets alongside sample surveys to produce statistics that provide reliable conclusions.
Ralphs, M & Ang L 2009, ‘Optimised geographies for data reporting: Zone design tools for census output geographies’, Statistics New Zealand Working Paper No 09-01, Statistics New Zealand, Wellington.
Hendrickson, L, Taylor, D, Ang, L, Cao, K, Nguyen, T & Soriano, F 2021, ‘The impact of persistent innovation on Australian firm growth, Prometheus, Vol. 37, No. 3, pp. 241-258.
Tam, S-M, Kim, J K, Ang, L & Pham, H 2021, ‘Mining the New Oil for Official Statistics’, in C Hill, P Biemer, T Buskirk, L Japec, A Kirchner, S Kolenikov & L Lyberg (eds), Big Data Meets Survey Science: A Collection of Innovative Methods. John Wiley & Sons, New Jersey, pp.339- 359.
Tam, S M, Trewin, A & Ang, L 2022, ‘Error analysis for hybrid estimates of proportion using big data’ Statistical Journal of the IAOS, Preprint, https://doi.org/10.3233/SJI-210924