Monitoring photochemical pollutants based on symbolic interval-valued data analysis

IF 1.4 4区计算机科学 Q2 STATISTICS & PROBABILITY

Advances in Data Analysis and Classification Pub Date : 2022-11-12 DOI:10.1007/s11634-022-00527-1

Liang-Ching Lin, Meihui Guo, Sangyeol Lee

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Abstract

This study considers monitoring photochemical pollutants for anomaly detection based on symbolic interval-valued data analysis. For this task, we construct control charts based on the principal component scores of symbolic interval-valued data. Herein, the symbolic interval-valued data are assumed to follow a normal distribution, and an approximate expectation formula of order statistics from the normal distribution is used in the univariate case to estimate the mean and variance via the method of moments. In addition, we consider the bivariate case wherein we use the maximum likelihood estimator calculated from the likelihood function derived under a bivariate copula. We also establish the procedures for the statistical control chart based on the univariate and bivariate interval-valued variables, and the procedures are potentially extendable to higher dimensional cases. Monte Carlo simulations and real data analysis using photochemical pollutants confirm the validity of the proposed method. The results particularly show the superiority over the conventional method that uses the averages to identify the date on which the abnormal maximum occurred.

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基于符号区间值数据分析的光化学污染物监测

本文研究了基于符号区间值数据分析的光化学污染物监测异常检测。为此，我们基于符号区间值数据的主成分分数构造控制图。本文假设符号区间值数据服从正态分布，在单变量情况下，采用正态分布阶统计量的近似期望公式，通过矩量法估计均值和方差。此外，我们还考虑了二元情况，其中我们使用由二元copula导出的似然函数计算的最大似然估计量。我们还建立了基于单变量和双变量区间值变量的统计控制图的程序，并且该程序有可能扩展到高维情况。蒙特卡罗模拟和使用光化学污染物的实际数据分析证实了该方法的有效性。结果特别表明，该方法优于使用平均值来确定异常最大值发生日期的传统方法。

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来源期刊

Advances in Data Analysis and Classification STATISTICS & PROBABILITY-

CiteScore

3.40

自引率

6.20%

发文量

审稿时长

>12 weeks

期刊介绍： The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.