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首页> 外文期刊>Journal of applied statistics >Monitoring foreclosure rates with a spatially risk-adjusted Bernoulli CUSUM chart for concurrent observations
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Monitoring foreclosure rates with a spatially risk-adjusted Bernoulli CUSUM chart for concurrent observations

机译：使用空间风险调整后的Bernoulli CUSUM图表监视止赎率，以进行同时观察

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摘要

Frequently in process monitoring, situations arise in which the order that events occur cannot be distinguished, motivating the need to accommodate multiple observations occurring at the same time, or concurrent observations. The risk-adjusted Bernoulli cumulative sum (CUSUM) control chart can be used to monitor the rate of an adverse event by fitting a risk-adjustment model, followed by a likelihood ratio-based scoring method that produces a statistic that can be monitored. In our paper, we develop a risk-adjusted Bernoulli CUSUM control chart for concurrent observations. Furthermore, we adopt a novel approach that uses a combined mixture model and kernel density estimation approach in order to perform risk-adjustment with regard to spatial location. Our proposed method allows for monitoring binary outcomes through time with multiple observations at each time point, where the chart is spatially adjusted for each Bernoulli observation's estimated probability of the adverse event. A simulation study is presented to assess the performance of the proposed monitoring scheme. We apply our method using data from Wayne County, Michigan between 2005 and 2014 to monitor the rate of foreclosure as a percentage of all housing transactions.
机译：在过程监视中，经常出现无法区分事件发生顺序的情况，从而激发了适应同时发生的多个观察或并发观察的需求。风险调整后的伯努利累积总和（CUSUM）控制图可用于通过拟合风险调整模型来监测不良事件的发生率，然后采用基于似然比的评分方法来产生可监控的统计数据。在我们的论文中，我们开发了经过风险调整的Bernoulli CUSUM控制图，用于同时观察。此外，我们采用一种新颖的方法，该方法使用混合的混合模型和核密度估计方法相结合，以便对空间位置进行风险调整。我们提出的方法允许通过在每个时间点进行多次观察来随时间监视二进制结果，其中针对每个伯努利观察的估计不良事件概率对图表进行空间调整。进行了仿真研究，以评估所提出的监视方案的性能。我们使用2005年至2014年间密歇根州韦恩县的数据来应用我们的方法，以监测止赎率在所有房屋交易中所占的百分比。

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