DIA-datasnooping and identifiability
In this contribution, we present and analyze datasnooping in the context of the DIA method. As the DIA method for the detection, identification and adaptation of mismodelling errors is concerned with estimation and testing, it is the combination of both that needs to be considered. This combination...
| Main Authors: | , |
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| Format: | Journal Article |
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Springer - Verlag
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/68229 |
| _version_ | 1848761777353916416 |
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| author | Zaminpardaz, S. Teunissen, Peter |
| author_facet | Zaminpardaz, S. Teunissen, Peter |
| author_sort | Zaminpardaz, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this contribution, we present and analyze datasnooping in the context of the DIA method. As the DIA method for the detection, identification and adaptation of mismodelling errors is concerned with estimation and testing, it is the combination of both that needs to be considered. This combination is rigorously captured by the DIA estimator. We discuss and analyze the DIA-datasnooping decision probabilities and the construction of the corresponding partitioning of misclosure space. We also investigate the circumstances under which two or more hypotheses are nonseparable in the identification step. By means of a theorem on the equivalence between the nonseparability of hypotheses and the inestimability of parameters, we demonstrate that one can forget about adapting the parameter vector for hypotheses that are nonseparable. However, as this concerns the complete vector and not necessarily functions of it, we also show that parameter functions may exist for which adaptation is still possible. It is shown how this adaptation looks like and how it changes the structure of the DIA estimator. To demonstrate the performance of the various elements of DIA-datasnooping, we apply the theory to some selected examples. We analyze how geometry changes in the measurement setup affect the testing procedure, by studying their partitioning of misclosure space, the decision probabilities and the minimal detectable and identifiable biases. The difference between these two minimal biases is highlighted by showing the difference between their corresponding contributing factors. We also show that if two alternative hypotheses, say (Formula presented.) and (Formula presented.), are nonseparable, the testing procedure may have different levels of sensitivity to (Formula presented.)-biases compared to the same (Formula presented.)-biases. |
| first_indexed | 2025-11-14T10:37:04Z |
| format | Journal Article |
| id | curtin-20.500.11937-68229 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:37:04Z |
| publishDate | 2018 |
| publisher | Springer - Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-682292019-02-04T06:06:54Z DIA-datasnooping and identifiability Zaminpardaz, S. Teunissen, Peter In this contribution, we present and analyze datasnooping in the context of the DIA method. As the DIA method for the detection, identification and adaptation of mismodelling errors is concerned with estimation and testing, it is the combination of both that needs to be considered. This combination is rigorously captured by the DIA estimator. We discuss and analyze the DIA-datasnooping decision probabilities and the construction of the corresponding partitioning of misclosure space. We also investigate the circumstances under which two or more hypotheses are nonseparable in the identification step. By means of a theorem on the equivalence between the nonseparability of hypotheses and the inestimability of parameters, we demonstrate that one can forget about adapting the parameter vector for hypotheses that are nonseparable. However, as this concerns the complete vector and not necessarily functions of it, we also show that parameter functions may exist for which adaptation is still possible. It is shown how this adaptation looks like and how it changes the structure of the DIA estimator. To demonstrate the performance of the various elements of DIA-datasnooping, we apply the theory to some selected examples. We analyze how geometry changes in the measurement setup affect the testing procedure, by studying their partitioning of misclosure space, the decision probabilities and the minimal detectable and identifiable biases. The difference between these two minimal biases is highlighted by showing the difference between their corresponding contributing factors. We also show that if two alternative hypotheses, say (Formula presented.) and (Formula presented.), are nonseparable, the testing procedure may have different levels of sensitivity to (Formula presented.)-biases compared to the same (Formula presented.)-biases. 2018 Journal Article http://hdl.handle.net/20.500.11937/68229 10.1007/s00190-018-1141-3 http://creativecommons.org/licenses/by/4.0/ Springer - Verlag fulltext |
| spellingShingle | Zaminpardaz, S. Teunissen, Peter DIA-datasnooping and identifiability |
| title | DIA-datasnooping and identifiability |
| title_full | DIA-datasnooping and identifiability |
| title_fullStr | DIA-datasnooping and identifiability |
| title_full_unstemmed | DIA-datasnooping and identifiability |
| title_short | DIA-datasnooping and identifiability |
| title_sort | dia-datasnooping and identifiability |
| url | http://hdl.handle.net/20.500.11937/68229 |