Regression model under skew-normal error with applications in predicting groundwater arsenic level in the Mekong Delta Region
Abstract Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression set-up, discussed a step...
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| Autor*in: |
Huynh, Uyen [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Environmental and ecological statistics - Springer US, 1994, 28(2021), 2 vom: 17. März, Seite 323-353 |
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| Übergeordnetes Werk: |
volume:28 ; year:2021 ; number:2 ; day:17 ; month:03 ; pages:323-353 |
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| DOI / URN: |
10.1007/s10651-021-00488-2 |
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OLC2125286130 |
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| 520 | |a Abstract Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression set-up, discussed a step by step approach on how to estimate all the model parameters, and show how naturally the resultant SND-based regression model can lead to a superior fitting to a given dataset. This generalization enhances the precision in predicting the future value of the response variable when the values of the independent (or input) variables are available. We validate the applicability of our proposed SND-based regression model by using a recently acquired dataset from the Mekong Delta Region (MDR) of Vietnam which had necessitated this study from a public health perspective. Using the existing survey data our proposed model allows all the stakeholders to better predict the groundwater arsenic level at a site easily, based on its geographic characteristics, in lieu of costly chemical analyses, which can be very beneficial to developing countries due to their resource constraints. | ||
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10.1007/s10651-021-00488-2 doi (DE-627)OLC2125286130 (DE-He213)s10651-021-00488-2-p DE-627 ger DE-627 rakwb eng 310 VZ Huynh, Uyen verfasserin aut Regression model under skew-normal error with applications in predicting groundwater arsenic level in the Mekong Delta Region 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression set-up, discussed a step by step approach on how to estimate all the model parameters, and show how naturally the resultant SND-based regression model can lead to a superior fitting to a given dataset. This generalization enhances the precision in predicting the future value of the response variable when the values of the independent (or input) variables are available. We validate the applicability of our proposed SND-based regression model by using a recently acquired dataset from the Mekong Delta Region (MDR) of Vietnam which had necessitated this study from a public health perspective. Using the existing survey data our proposed model allows all the stakeholders to better predict the groundwater arsenic level at a site easily, based on its geographic characteristics, in lieu of costly chemical analyses, which can be very beneficial to developing countries due to their resource constraints. Bootstrap method Least squares method Normal distribution Prediction mean absolute error Prediction mean squared error Skew-normal distribution Pal, Nabendu (orcid)0000-0001-6243-4988 aut Nguyen, Man aut Enthalten in Environmental and ecological statistics Springer US, 1994 28(2021), 2 vom: 17. März, Seite 323-353 (DE-627)188856781 (DE-600)1284261-8 (DE-576)067290140 1352-8505 nnns volume:28 year:2021 number:2 day:17 month:03 pages:323-353 https://doi.org/10.1007/s10651-021-00488-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL AR 28 2021 2 17 03 323-353 |
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10.1007/s10651-021-00488-2 doi (DE-627)OLC2125286130 (DE-He213)s10651-021-00488-2-p DE-627 ger DE-627 rakwb eng 310 VZ Huynh, Uyen verfasserin aut Regression model under skew-normal error with applications in predicting groundwater arsenic level in the Mekong Delta Region 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression set-up, discussed a step by step approach on how to estimate all the model parameters, and show how naturally the resultant SND-based regression model can lead to a superior fitting to a given dataset. This generalization enhances the precision in predicting the future value of the response variable when the values of the independent (or input) variables are available. We validate the applicability of our proposed SND-based regression model by using a recently acquired dataset from the Mekong Delta Region (MDR) of Vietnam which had necessitated this study from a public health perspective. Using the existing survey data our proposed model allows all the stakeholders to better predict the groundwater arsenic level at a site easily, based on its geographic characteristics, in lieu of costly chemical analyses, which can be very beneficial to developing countries due to their resource constraints. Bootstrap method Least squares method Normal distribution Prediction mean absolute error Prediction mean squared error Skew-normal distribution Pal, Nabendu (orcid)0000-0001-6243-4988 aut Nguyen, Man aut Enthalten in Environmental and ecological statistics Springer US, 1994 28(2021), 2 vom: 17. März, Seite 323-353 (DE-627)188856781 (DE-600)1284261-8 (DE-576)067290140 1352-8505 nnns volume:28 year:2021 number:2 day:17 month:03 pages:323-353 https://doi.org/10.1007/s10651-021-00488-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL AR 28 2021 2 17 03 323-353 |
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10.1007/s10651-021-00488-2 doi (DE-627)OLC2125286130 (DE-He213)s10651-021-00488-2-p DE-627 ger DE-627 rakwb eng 310 VZ Huynh, Uyen verfasserin aut Regression model under skew-normal error with applications in predicting groundwater arsenic level in the Mekong Delta Region 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression set-up, discussed a step by step approach on how to estimate all the model parameters, and show how naturally the resultant SND-based regression model can lead to a superior fitting to a given dataset. This generalization enhances the precision in predicting the future value of the response variable when the values of the independent (or input) variables are available. We validate the applicability of our proposed SND-based regression model by using a recently acquired dataset from the Mekong Delta Region (MDR) of Vietnam which had necessitated this study from a public health perspective. Using the existing survey data our proposed model allows all the stakeholders to better predict the groundwater arsenic level at a site easily, based on its geographic characteristics, in lieu of costly chemical analyses, which can be very beneficial to developing countries due to their resource constraints. Bootstrap method Least squares method Normal distribution Prediction mean absolute error Prediction mean squared error Skew-normal distribution Pal, Nabendu (orcid)0000-0001-6243-4988 aut Nguyen, Man aut Enthalten in Environmental and ecological statistics Springer US, 1994 28(2021), 2 vom: 17. März, Seite 323-353 (DE-627)188856781 (DE-600)1284261-8 (DE-576)067290140 1352-8505 nnns volume:28 year:2021 number:2 day:17 month:03 pages:323-353 https://doi.org/10.1007/s10651-021-00488-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL AR 28 2021 2 17 03 323-353 |
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10.1007/s10651-021-00488-2 doi (DE-627)OLC2125286130 (DE-He213)s10651-021-00488-2-p DE-627 ger DE-627 rakwb eng 310 VZ Huynh, Uyen verfasserin aut Regression model under skew-normal error with applications in predicting groundwater arsenic level in the Mekong Delta Region 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression set-up, discussed a step by step approach on how to estimate all the model parameters, and show how naturally the resultant SND-based regression model can lead to a superior fitting to a given dataset. This generalization enhances the precision in predicting the future value of the response variable when the values of the independent (or input) variables are available. We validate the applicability of our proposed SND-based regression model by using a recently acquired dataset from the Mekong Delta Region (MDR) of Vietnam which had necessitated this study from a public health perspective. Using the existing survey data our proposed model allows all the stakeholders to better predict the groundwater arsenic level at a site easily, based on its geographic characteristics, in lieu of costly chemical analyses, which can be very beneficial to developing countries due to their resource constraints. Bootstrap method Least squares method Normal distribution Prediction mean absolute error Prediction mean squared error Skew-normal distribution Pal, Nabendu (orcid)0000-0001-6243-4988 aut Nguyen, Man aut Enthalten in Environmental and ecological statistics Springer US, 1994 28(2021), 2 vom: 17. März, Seite 323-353 (DE-627)188856781 (DE-600)1284261-8 (DE-576)067290140 1352-8505 nnns volume:28 year:2021 number:2 day:17 month:03 pages:323-353 https://doi.org/10.1007/s10651-021-00488-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL AR 28 2021 2 17 03 323-353 |
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Abstract Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression set-up, discussed a step by step approach on how to estimate all the model parameters, and show how naturally the resultant SND-based regression model can lead to a superior fitting to a given dataset. This generalization enhances the precision in predicting the future value of the response variable when the values of the independent (or input) variables are available. We validate the applicability of our proposed SND-based regression model by using a recently acquired dataset from the Mekong Delta Region (MDR) of Vietnam which had necessitated this study from a public health perspective. Using the existing survey data our proposed model allows all the stakeholders to better predict the groundwater arsenic level at a site easily, based on its geographic characteristics, in lieu of costly chemical analyses, which can be very beneficial to developing countries due to their resource constraints. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Abstract Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression set-up, discussed a step by step approach on how to estimate all the model parameters, and show how naturally the resultant SND-based regression model can lead to a superior fitting to a given dataset. This generalization enhances the precision in predicting the future value of the response variable when the values of the independent (or input) variables are available. We validate the applicability of our proposed SND-based regression model by using a recently acquired dataset from the Mekong Delta Region (MDR) of Vietnam which had necessitated this study from a public health perspective. Using the existing survey data our proposed model allows all the stakeholders to better predict the groundwater arsenic level at a site easily, based on its geographic characteristics, in lieu of costly chemical analyses, which can be very beneficial to developing countries due to their resource constraints. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression set-up, discussed a step by step approach on how to estimate all the model parameters, and show how naturally the resultant SND-based regression model can lead to a superior fitting to a given dataset. This generalization enhances the precision in predicting the future value of the response variable when the values of the independent (or input) variables are available. We validate the applicability of our proposed SND-based regression model by using a recently acquired dataset from the Mekong Delta Region (MDR) of Vietnam which had necessitated this study from a public health perspective. Using the existing survey data our proposed model allows all the stakeholders to better predict the groundwater arsenic level at a site easily, based on its geographic characteristics, in lieu of costly chemical analyses, which can be very beneficial to developing countries due to their resource constraints. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Regression model under skew-normal error with applications in predicting groundwater arsenic level in the Mekong Delta Region |
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