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| Main Author: | |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.00445 |
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| _version_ | 1866913198460895232 |
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| author | Wiens, Douglas P. |
| author_facet | Wiens, Douglas P. |
| contents | We present a result according to which certain functions of covariance matrices are maximized at scalar multiples of the identity matrix. This is used to show that experimental designs that are optimal under an assumption of independent, homoscedastic responses can be minimax robust, in broad classes of alternate covariance structures. In particular it can justify the common practice of disregarding possible dependence, or heteroscedasticity, at the design stage of an experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00445 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Note on Minimax Robustness of Designs Against Correlated or Heteroscedastic Responses Wiens, Douglas P. Statistics Theory Primary 62K05, Secondary 62G35 We present a result according to which certain functions of covariance matrices are maximized at scalar multiples of the identity matrix. This is used to show that experimental designs that are optimal under an assumption of independent, homoscedastic responses can be minimax robust, in broad classes of alternate covariance structures. In particular it can justify the common practice of disregarding possible dependence, or heteroscedasticity, at the design stage of an experiment. |
| title | A Note on Minimax Robustness of Designs Against Correlated or Heteroscedastic Responses |
| topic | Statistics Theory Primary 62K05, Secondary 62G35 |
| url | https://arxiv.org/abs/2310.00445 |