Sign-constrained linear regression for prediction of microbe concentration based on water quality datasets.
Sign-constrained linear regression for prediction of microbe concentration based on water quality datasets.
复制标题
基于水质数据集预测微生物浓度的符号约束线性回归。
DOI：
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发表时间：
2019
影响因子：
2.3
通讯作者：
D. Sano
中科院分区：
文献类型：
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作者：
Tsuyoshi Kato;Ayano Kobayashi;Wakana Oishi;Syun;S. Okabe;Naoya Ohta;Mohan Amarasiri;D. Sano
这项研究提出了一种根据环境参数估算水中环境污染物（例如病原体）浓度的新方法。这项研究的科学独特性是通过应用领域知识来防止模型拟合中的过度一致性，领域知识是关于响应和解释变量之间相关性的积累的科学知识。使用符号约束来表达领域知识，并研究符号约束对使用审查数据集的预测性能的影响。结果，我们证实与传统的无符号方法相比，符号约束使预测更加准确。这项研究最显着的技术贡献是发现符号约束可以纳入托比特分析中相关系数的估计中。我们开发了有效且数值稳定的算法，用于在符号约束下将模型拟合到数据集。这种新颖的算法适用于各种污染物污染水平的预测，包括水中的病原体浓度。
This study presents a novel methodology for estimating the concentration of environmental pollutants in water, such as pathogens, based on environmental parameters. The scientific uniqueness of this study is the prevention of excess conformity in the model fitting by applying domain knowledge, which is the accumulated scientific knowledge regarding the correlations between response and explanatory variables. Sign constraints were used to express domain knowledge, and the effect of the sign constraints on the prediction performance using censored datasets was investigated. As a result, we confirmed that sign constraints made prediction more accurate compared to conventional sign-free approaches. The most remarkable technical contribution of this study is the finding that the sign constraints can be incorporated in the estimation of the correlation coefficient in Tobit analysis. We developed effective and numerically stable algorithms for fitting a model to datasets under the sign constraints. This novel algorithm is applicable to a wide variety of the prediction of pollutant contamination level, including the pathogen concentrations in water.
影响因子：
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作者：
Tran Huynh;Ramachandran, Gurumurthy;Stewart, Patricia A.
通讯作者：
Stewart, Patricia A.