Abstract Change-point detection in categorical time series has recently gained attention as statistical models incorporating change-points are common in practice, especially in the area of biomedicine. In this article, we propose a sequential change-point detection procedure based on the partial likelihood score process for the detection of changes in the coefficients of multinomial logistic regression model. The asymptotic results are presented under both the null of no change and the alternative of changes in coefficients. We carry out a Monte Carlo experiment to evaluate the empirical size of the proposed procedure as well as its average run length. We illustrate the method by using data on a DNA sequence. Monte Carlo experiments and real data analysis demonstrate the effectiveness of the proposed procedure.

中文翻译：

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多项逻辑回归模型中的顺序变化点检测

摘要 分类时间序列中的变化点检测最近受到关注，因为结合变化点的统计模型在实践中很常见，尤其是在生物医学领域。在本文中，我们提出了一种基于偏似然评分过程的顺序变化点检测程序，用于检测多项逻辑回归模型的系数变化。渐近结果在没有变化的零值和系数变化的替代方案下呈现。我们进行了蒙特卡罗实验来评估所提出程序的经验大小及其平均运行长度。我们通过使用 DNA 序列数据来说明该方法。蒙特卡罗实验和真实数据分析证明了所提出程序的有效性。