Modified Poisson Regression Analysis of Grouped and Right-Censored Counts
针对分组右删失计数数据，推导了修正泊松估计量及其渐近性质，开发了混合线搜索算法进行参数推断，并通过美国药物使用调查数据验证了该方法相比有序逻辑模型的优势。
Abstract Grouped and right-censored (GRC) counts are widely used in criminology, demography, epidemiology, marketing, sociology, psychology and other related disciplines to study behavioural and event frequencies, especially when sensitive research topics or individuals with possibly lower cognitive capacities are at stake. Yet, the co-existence of grouping and right-censoring poses major difficulties in regression analysis. To implement generalised linear regression of GRC counts, we derive modified Poisson estimators and their asymptotic properties, develop a hybrid line search algorithm for parameter inference, demonstrate the finite-sample performance of these estimators via simulation, and evaluate its empirical applicability based on survey data of drug use in America. This method has a clear methodological advantage over the ordered logistic model for analysing GRC counts.