Estimating Heterogeneity in the Probabilities of Enumeration for Dual-System Estimation
展示了如何用条件逻辑回归估计普查中被枚举的概率，并应用于1990年美国后枚举调查，可辅助估计人口规模和相关性偏差，尤其适用于处理连续解释变量和迁移问题。
Abstract We show how conditional logistic regression can be used to estimate the probability of being enumerated in a census and apply the model to the 1990 Post-Enumeration Survey (PES) in the United States. The estimates can be used in the estimation of population size and the estimation of correlation bias, for example. Unlike the classical stratification approach, the logistic approach permits the use of continuous explanatory variables. Model choice can be based on the standard techniques of the generalized linear models. We discuss some special problems caused by the fact that the PES sample area is open to migration between the captures. We also consider the effect of data errors in estimation. We characterize hard-to-enumerate populations and give some tentative estimates of correlation bias.