2 citations · 2 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
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…
Dynamic Mini Max Design and Sequential HB Inference for Repeated Surveys
Siu-Ming Tam
TThis paper develops a Dynamic Mini-Max (DMM) framework for repeated surveys comprising a Dynamic Mini-Max Design and a Sequential Hierarchical Bayes Update (SHBU). The DMM jointly…
Post-Hoc Inference of Cross-Classified Statistics from Hierarchical Bayes Survey Weights
Siu-Ming Tam
Tam [2026] shows that combining Bethel multivariate allocation with Hierarchical Bayes (HB) small area models can substantially reduce survey sample sizes while maintaining domain-…
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 linkage bias-correction for estimators using iterated bootstraps
Siu-Ming Tam, Min Wang, Alicia Rambaldi +1
By amalgamating data from disparate sources, the resulting integrated dataset becomes a valuable resource for statistical analysis. In probabilistic record linkage, the effectivene…
A note on the optimum allocation of resources to follow up unit nonrespondents in probability
Su-Ming Tam, Anders Holmberg, Summer Wang
Common practice to address nonresponse in probability surveys in National Statistical Offices is to follow up every nonrespondent with a view to lifting response rates. As response…