Multi-target Tobit Models for Completing Water Quality Data

• Item type: Trans(1)
• 公開日: 2024-03-25
• タイトル: Multi-target Tobit Models for Completing Water Quality Data
• タイトル / 言語: en
• タイトル: Multi-target Tobit Models for Completing Water Quality Data
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [オリジナル論文] water quality data, censoring, data imputation, Tobit model, probabilistic model, linear regression
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Science and Technology, Gunma University
• 著者所属: Department of Civil and Environmental Engineering, Tohoku University
• 著者所属: The University of Tokyo
• 著者所属: Graduate School of Science and Technology, Gunma University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Technology, Gunma University
• 著者所属(英): en
• 著者所属(英): Department of Civil and Environmental Engineering, Tohoku University
• 著者所属(英): en
• 著者所属(英): The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Technology, Gunma University
• 著者名: Yuya, Takada; Daisuke, Sano; Syun-suke, Kadoya; Tsuyoshi, Kato
• 著者名(英): Yuya, Takada; Daisuke, Sano; Syun-suke, Kadoya; Tsuyoshi, Kato
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Monitoring microbiological behaviors in water is crucial to manage public health risk from waterborne pathogens, although quantifying the concentrations of microbiological organisms in water is still challenging because concentrations of many pathogens in water samples may often be below the quantification limit, producing censoring data. To enable statistical analysis based on quantitative values, the true values of non-detected measurements are required to be estimated with high precision. Tobit model is a well-known linear regression model for analyzing censored data. One drawback of the Tobit model is that only the target variable is allowed to be censored. In this study, we devised a novel extension of the classical Tobit model, called the multi-target Tobit model, to handle multiple censored variables simultaneously by introducing multiple target variables. For fitting the new model, a numerical stable optimization algorithm was developed based on elaborate theories. Experiments conducted using several real-world water quality datasets provided an evidence that estimating multiple columns jointly gains a great advantage over estimating them separately.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Monitoring microbiological behaviors in water is crucial to manage public health risk from waterborne pathogens, although quantifying the concentrations of microbiological organisms in water is still challenging because concentrations of many pathogens in water samples may often be below the quantification limit, producing censoring data. To enable statistical analysis based on quantitative values, the true values of non-detected measurements are required to be estimated with high precision. Tobit model is a well-known linear regression model for analyzing censored data. One drawback of the Tobit model is that only the target variable is allowed to be censored. In this study, we devised a novel extension of the classical Tobit model, called the multi-target Tobit model, to handle multiple censored variables simultaneously by introducing multiple target variables. For fitting the new model, a numerical stable optimization algorithm was developed based on elaborate theories. Experiments conducted using several real-world water quality datasets provided an evidence that estimating multiple columns jointly gains a great advantage over estimating them separately.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464803
• 書誌情報: 情報処理学会論文誌数理モデル化と応用（TOM）; 巻 17, 号 2, p. 21-29, 発行日 2024-03-25
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7780
• 出版者 / 言語: ja
• 出版者: 情報処理学会