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Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis
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| Zeitschriftentitel: | International Journal of Environmental Research and Public Health |
|---|---|
| Personen und Körperschaften: | , , |
| In: | International Journal of Environmental Research and Public Health, 18, 2021, 4, S. 1741 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MDPI AG
|
| Schlagwörter: |
| author_facet |
Hermanussen, Michael Aßmann, Christian Groth, Detlef Hermanussen, Michael Aßmann, Christian Groth, Detlef |
|---|---|
| author |
Hermanussen, Michael Aßmann, Christian Groth, Detlef |
| spellingShingle |
Hermanussen, Michael Aßmann, Christian Groth, Detlef International Journal of Environmental Research and Public Health Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis Health, Toxicology and Mutagenesis Public Health, Environmental and Occupational Health |
| author_sort |
hermanussen, michael |
| spelling |
Hermanussen, Michael Aßmann, Christian Groth, Detlef 1660-4601 MDPI AG Health, Toxicology and Mutagenesis Public Health, Environmental and Occupational Health http://dx.doi.org/10.3390/ijerph18041741 <jats:p>(1) Background: We present a new statistical approach labeled as “St. Nicolas House Analysis” (SNHA) for detecting and visualizing extensive interactions among variables. (2) Method: We rank absolute bivariate correlation coefficients in descending order according to magnitude and create hierarchic “association chains” defined by sequences where reversing start and end point does not alter the ordering of elements. Association chains are used to characterize dependence structures of interacting variables by a graph. (3) Results: SNHA depicts association chains in highly, but also in weakly correlated data, and is robust towards spurious accidental associations. Overlapping association chains can be visualized as network graphs. Between independent variables significantly fewer associations are detected compared to standard correlation or linear model-based approaches. (4) Conclusion: We propose reversible association chains as a principle to detect dependencies among variables. The proposed method can be conceptualized as a non-parametric statistical method. It is especially suited for secondary data analysis as only aggregate information such as correlations matrices are required. The analysis provides an initial approach for clarifying potential associations that may be subject to subsequent hypothesis testing.</jats:p> Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis International Journal of Environmental Research and Public Health |
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| title |
Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| title_unstemmed |
Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| title_full |
Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
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Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| title_full_unstemmed |
Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| title_short |
Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| title_sort |
chain reversion for detecting associations in interacting variables—st. nicolas house analysis |
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Health, Toxicology and Mutagenesis Public Health, Environmental and Occupational Health |
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http://dx.doi.org/10.3390/ijerph18041741 |
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2021 |
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1741 |
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<jats:p>(1) Background: We present a new statistical approach labeled as “St. Nicolas House Analysis” (SNHA) for detecting and visualizing extensive interactions among variables. (2) Method: We rank absolute bivariate correlation coefficients in descending order according to magnitude and create hierarchic “association chains” defined by sequences where reversing start and end point does not alter the ordering of elements. Association chains are used to characterize dependence structures of interacting variables by a graph. (3) Results: SNHA depicts association chains in highly, but also in weakly correlated data, and is robust towards spurious accidental associations. Overlapping association chains can be visualized as network graphs. Between independent variables significantly fewer associations are detected compared to standard correlation or linear model-based approaches. (4) Conclusion: We propose reversible association chains as a principle to detect dependencies among variables. The proposed method can be conceptualized as a non-parametric statistical method. It is especially suited for secondary data analysis as only aggregate information such as correlations matrices are required. The analysis provides an initial approach for clarifying potential associations that may be subject to subsequent hypothesis testing.</jats:p> |
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| author | Hermanussen, Michael, Aßmann, Christian, Groth, Detlef |
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| description | <jats:p>(1) Background: We present a new statistical approach labeled as “St. Nicolas House Analysis” (SNHA) for detecting and visualizing extensive interactions among variables. (2) Method: We rank absolute bivariate correlation coefficients in descending order according to magnitude and create hierarchic “association chains” defined by sequences where reversing start and end point does not alter the ordering of elements. Association chains are used to characterize dependence structures of interacting variables by a graph. (3) Results: SNHA depicts association chains in highly, but also in weakly correlated data, and is robust towards spurious accidental associations. Overlapping association chains can be visualized as network graphs. Between independent variables significantly fewer associations are detected compared to standard correlation or linear model-based approaches. (4) Conclusion: We propose reversible association chains as a principle to detect dependencies among variables. The proposed method can be conceptualized as a non-parametric statistical method. It is especially suited for secondary data analysis as only aggregate information such as correlations matrices are required. The analysis provides an initial approach for clarifying potential associations that may be subject to subsequent hypothesis testing.</jats:p> |
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| spelling | Hermanussen, Michael Aßmann, Christian Groth, Detlef 1660-4601 MDPI AG Health, Toxicology and Mutagenesis Public Health, Environmental and Occupational Health http://dx.doi.org/10.3390/ijerph18041741 <jats:p>(1) Background: We present a new statistical approach labeled as “St. Nicolas House Analysis” (SNHA) for detecting and visualizing extensive interactions among variables. (2) Method: We rank absolute bivariate correlation coefficients in descending order according to magnitude and create hierarchic “association chains” defined by sequences where reversing start and end point does not alter the ordering of elements. Association chains are used to characterize dependence structures of interacting variables by a graph. (3) Results: SNHA depicts association chains in highly, but also in weakly correlated data, and is robust towards spurious accidental associations. Overlapping association chains can be visualized as network graphs. Between independent variables significantly fewer associations are detected compared to standard correlation or linear model-based approaches. (4) Conclusion: We propose reversible association chains as a principle to detect dependencies among variables. The proposed method can be conceptualized as a non-parametric statistical method. It is especially suited for secondary data analysis as only aggregate information such as correlations matrices are required. The analysis provides an initial approach for clarifying potential associations that may be subject to subsequent hypothesis testing.</jats:p> Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis International Journal of Environmental Research and Public Health |
| spellingShingle | Hermanussen, Michael, Aßmann, Christian, Groth, Detlef, International Journal of Environmental Research and Public Health, Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis, Health, Toxicology and Mutagenesis, Public Health, Environmental and Occupational Health |
| title | Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| title_full | Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| title_fullStr | Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| title_full_unstemmed | Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| title_short | Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| title_sort | chain reversion for detecting associations in interacting variables—st. nicolas house analysis |
| title_unstemmed | Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis |
| topic | Health, Toxicology and Mutagenesis, Public Health, Environmental and Occupational Health |
| url | http://dx.doi.org/10.3390/ijerph18041741 |