9 papers
Responsible AI and Algorithmic Adoption in Methodology Development for National Statistical Offices
Siu-Ming Tam
To meet growing demand for granular demographic and socioeconomic indicators under tighter budgets, national statistical offices must continually develop new methods. These include…
A Total Statistical Error Framework for Comparing Census Data Collection Methods
Siu-MIng Tam, Anders Holmberg
Population censuses increasingly rely on imputation to assign usual-residence addresses for non-responding dwellings, yet no formal statistical framework has existed for comparing…
National Versus Domain: Coverage Properties of HB Credible Intervals Under Survey Redesign
Siu-Ming Tam
A companion paper to Tam (2026) reports an extended Monte Carlo (MC) study examining the frequentist coverage of 95% hierarchical Bayes (HB) credible intervals at the national and…
Bayesian Seasonal Adjustment for Survey Time Series
Siu-Ming Tam
Seasonal adjustment procedures used by national statistical offices -- X-11 and X-12-ARIMA -- treat each survey estimate as an exact observation, discarding the accompanying standa…
More with Less -- Bethel Allocation and Precision-Preserving Sample Size Reduction via Hierarchical Bayes Modelling
Siu-Ming Tam
Statistical offices face a familiar and intensifying dilemma: rising demand for detailed regional and domain-level estimates under budgets that are fixed or shrinking. National sta…
On design-unbiased algorithmic Machine Learning
Li-Chun Zhang, Siu-Ming Tam, Luis Sanguiao-Sande +2
Machine Learning (ML) algorithms, such as k-Nearest Neighbours (kNN) or random forest, eschew the ideal of true data models in favour of predictive performance. However, minimising…