Premium
Nonparametric Estimation of the Number of Drug Users in Hong Kong Using Repeated Multiple Lists
Author(s) -
Huggins Richard M.,
Yip Paul S.F.,
Stoklosa Jakub
Publication year - 2016
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12149
Subject(s) - smoothing , nonparametric statistics , mathematics , covariate , smoothing spline , estimation , statistics , point estimation , feature selection , spline (mechanical) , econometrics , computer science , machine learning , management , structural engineering , engineering , economics , bilinear interpolation , spline interpolation
Summary We update a previous approach to the estimation of the size of an open population when there are multiple lists at each time point. Our motivation is 35 years of longitudinal data on the detection of drug users by the Central Registry of Drug Abuse in Hong Kong. We develop a two‐stage smoothing spline approach. This gives a flexible and easily implemented alternative to the previous method which was based on kernel smoothing. The new method retains the property of reducing the variability of the individual estimates at each time point. We evaluate the new method by means of a simulation study that includes an examination of the effects of variable selection. The new method is then applied to data collected by the Central Registry of Drug Abuse. The parameter estimates obtained are compared with the well known Jolly–Seber estimates based on single capture methods.