Inferring Population Size: Extending the Multiplier Method to Incorporate Multiple Traits with a Likelihood-Based Approach

• Activity Number: 201 - Essential Recent Papers in Stat
• Type: Invited
• Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
• Sponsor: International Statistical Institute
• Author(s): Vivian Meng*
• Companies: McGill University

Abstract

Estimating population size is an important task for resource planning and policy making. One method is the "multiplier method" that uses information about a binary trait to infer the size of a population. This paper presents a likelihood-based estimator that generalizes the multiplier method to accommodate multiple traits as well as any number of categories in a trait. To provide guidelines for study design, we quantify the advantage of using multiple traits (multiple multipliers) by studying the estimator's asymptotic standard deviation (ASD). Inclusion of multiple traits reduces the ASD most effectively when the traits are uncorrelated and of low prevalence (roughly less than 10%), but the amount of reduction in ASD diminishes when the number of traits becomes large. A Bayesian implementation of our method is applied to both simulated data and real data pertaining to an injection-drug user population.