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本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

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分类依据与纠错

AI 根据所提供的论文材料归类:摘要核心是捕获—再捕获总体规模估计,商业访问数据并不直接构成离散选择、因果推断或其他所列细分研究。

反馈分类问题

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Mamadou Yauck
Louis-Paul Rivest
Greg Rothman

移动应用激活数据的捕获—再捕获方法

研究问题
如何利用商业地点的移动应用激活记录,估计一段时期内到访用户总量,并处理未被平台记录的访问及估计不确定性?
方法与思路
作者将设备用户与时间区间构造成捕获—再捕获数据,采用嵌套捕获的稳健设计,并提出参数估计算法和参数自助法方差估计。
材料陈述的贡献
摘要称,新算法适用于较多捕获时点,并提供扩展稳健设计的估计方法和理论结果,以汽车经销商设备访问数据展示。
阅读关注点
应核对设备与用户的对应关系、激活记录的捕获机制、稳健设计的层级划分,以及总用户数估计和自助法的适用条件。
阅读边界
仅据 OpenAlex 索引摘要,稿件版本未注明,不能推断刊本变化;捕获假设、估计偏差与方差表现未说明,设备访问也未必等同实际客流。

原文摘要

This work is concerned with the analysis of marketing data on the activation of applications (apps) on mobile devices. Each application has a hashed identification number that is specific to the device on which it has been installed. This number can be registered by a platform at each activation of the application. Activations on the same device are linked together using the identification number. By focusing on activations that took place at a business location, one can create a capture-recapture dataset about devices, that is, users, that “visited” the business: the units are owners of mobile devices and the capture occasions are time intervals such as days. A unit is captured when she activates an application, provided that this activation is recorded by the platform providing the data. Statistical capture-recapture techniques can be applied to the app data to estimate the total number of users that visited the business over a time period, thereby providing an indirect estimate of foot traffic. This article argues that the robust design, a method for dealing with a nested mark-recapture experiment, can be used in this context. A new algorithm for estimating the parameters of a robust design with a fairly large number of capture occasions and a simple parametric bootstrap variance estimator are proposed. Moreover, new estimation methods and new theoretical results are introduced for a wider application of the robust design. This is used to analyze a dataset about the mobile devices that visited the auto-dealerships of a major auto brand in a U.S. metropolitan area over a period of 1 year and a half. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

笔记与分组