The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A contaminação dos recursos hídricos com microrganismos patogênicos excretados nas fezes humanas é um problema de saúde pública mundial. A vigilância da contaminação fecal é comumente realizada por monitoramento de rotina para um único tipo ou alguns tipos de microrganismo(s). Para conceber uma rotina viável de monitorização periódica e para controlar os riscos de exposição a agentes patogénicos, é necessário estabelecer algoritmos estatísticos fiáveis para inferir correlações entre concentrações de microrganismos na água. Além disso, como os agentes patogénicos estão frequentemente presentes em baixas concentrações, é provável que algumas contaminações estejam abaixo do limite de detecção. Isso produz um conjunto de dados censurados à esquerda aos pares e complica o cálculo dos coeficientes de correlação. Os erros de estimativa de correlação podem ser menores se os valores não detectados forem melhor imputados. Para obter melhores imputações, utilizamos informações secundárias e desenvolvemos uma nova técnica, a modelo Tobit assimétrico que é uma extensão do modelo Tobit para que o conhecimento do domínio possa ser explorado de forma eficaz ao ajustar o modelo a um conjunto de dados censurado. Os resultados empíricos demonstram que a imputação com conhecimento de domínio é eficaz para esta tarefa.
HongYuan CAO
Gunma University
Tsuyoshi KATO
Gunma University
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HongYuan CAO, Tsuyoshi KATO, "Asymmetric Tobit Analysis for Correlation Estimation from Censored Data" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 10, pp. 1632-1639, October 2021, doi: 10.1587/transinf.2021EDP7022.
Abstract: Contamination of water resources with pathogenic microorganisms excreted in human feces is a worldwide public health concern. Surveillance of fecal contamination is commonly performed by routine monitoring for a single type or a few types of microorganism(s). To design a feasible routine for periodic monitoring and to control risks of exposure to pathogens, reliable statistical algorithms for inferring correlations between concentrations of microorganisms in water need to be established. Moreover, because pathogens are often present in low concentrations, some contaminations are likely to be under a detection limit. This yields a pairwise left-censored dataset and complicates computation of correlation coefficients. Errors of correlation estimation can be smaller if undetected values are imputed better. To obtain better imputations, we utilize side information and develop a new technique, the asymmetric Tobit model which is an extension of the Tobit model so that domain knowledge can be exploited effectively when fitting the model to a censored dataset. The empirical results demonstrate that imputation with domain knowledge is effective for this task.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7022/_p
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@ARTICLE{e104-d_10_1632,
author={HongYuan CAO, Tsuyoshi KATO, },
journal={IEICE TRANSACTIONS on Information},
title={Asymmetric Tobit Analysis for Correlation Estimation from Censored Data},
year={2021},
volume={E104-D},
number={10},
pages={1632-1639},
abstract={Contamination of water resources with pathogenic microorganisms excreted in human feces is a worldwide public health concern. Surveillance of fecal contamination is commonly performed by routine monitoring for a single type or a few types of microorganism(s). To design a feasible routine for periodic monitoring and to control risks of exposure to pathogens, reliable statistical algorithms for inferring correlations between concentrations of microorganisms in water need to be established. Moreover, because pathogens are often present in low concentrations, some contaminations are likely to be under a detection limit. This yields a pairwise left-censored dataset and complicates computation of correlation coefficients. Errors of correlation estimation can be smaller if undetected values are imputed better. To obtain better imputations, we utilize side information and develop a new technique, the asymmetric Tobit model which is an extension of the Tobit model so that domain knowledge can be exploited effectively when fitting the model to a censored dataset. The empirical results demonstrate that imputation with domain knowledge is effective for this task.},
keywords={},
doi={10.1587/transinf.2021EDP7022},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Asymmetric Tobit Analysis for Correlation Estimation from Censored Data
T2 - IEICE TRANSACTIONS on Information
SP - 1632
EP - 1639
AU - HongYuan CAO
AU - Tsuyoshi KATO
PY - 2021
DO - 10.1587/transinf.2021EDP7022
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2021
AB - Contamination of water resources with pathogenic microorganisms excreted in human feces is a worldwide public health concern. Surveillance of fecal contamination is commonly performed by routine monitoring for a single type or a few types of microorganism(s). To design a feasible routine for periodic monitoring and to control risks of exposure to pathogens, reliable statistical algorithms for inferring correlations between concentrations of microorganisms in water need to be established. Moreover, because pathogens are often present in low concentrations, some contaminations are likely to be under a detection limit. This yields a pairwise left-censored dataset and complicates computation of correlation coefficients. Errors of correlation estimation can be smaller if undetected values are imputed better. To obtain better imputations, we utilize side information and develop a new technique, the asymmetric Tobit model which is an extension of the Tobit model so that domain knowledge can be exploited effectively when fitting the model to a censored dataset. The empirical results demonstrate that imputation with domain knowledge is effective for this task.
ER -