Modeling Response Bias in Count: A Structural Approach With an Application to the National Crime Victimization Survey Data
Modeling Response Bias in Count: A Structural Approach With an Application to the National Crime Victimization Survey Data
复制标题
计数响应偏差建模：一种应用于全国犯罪受害调查数据的结构方法
DOI：
--
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Jie Q. Guo
中科院分区：
文献类型：
--
作者：
Tong Li;P. Trivedi;Jie Q. Guo
本文考虑当响应是计数时对响应偏差进行建模。作者采用了一种“结构方法”，使用广义负二项混合泊松分布来模拟误报计数，假设真实响应的分布遵循负二项分布。该模型可以被解释为“停和”模型。提出了一种模拟极大似然估计，并通过Monte Carlo模拟研究了它的有限样本性能。然后，该方法被应用到分析学校的受害数据来自全国犯罪受害调查，这使作者能够确定个人和学校相关的特点，可能有助于学校犯罪受害和重复受害的报告数量的可能的偏见。作者发现，对于报告的盗窃数量，大约12%的受访者多报了这些数字，其中大多数人实际上在学校没有任何物品被盗。
This article considers modeling response bias when the response is a count. The authors adopt a “structural approach” by using a generalized negative binomial mixture of Poisson distribution to model misreported counts, assuming that the distribution of the true response follows a negative binomial distribution. The model may be interpreted as a “stopped-sum” model. A simulated maximum likelihood estimator is proposed, and its finite sample performance is investigated through Monte Carlo simulations. The approach is then applied to analyzing school victimization data drawn from the National Crime Victimization Survey, which allows the authors to identify the individual- and school-related characteristics that could contribute to school crime victimization and to the possible biases on the reported number of repeat victimizations. The authors find that for the reported number of thefts, about 12 percent of respondents overreport the numbers, most of whom actually have not had any item stolen at school.