Inference in generalized linear models with robustness to misspecified variances
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866917775029567488 |
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| author | De Santis, Riccardo Goeman, Jelle J. Hemerik, Jesse Davenport, Samuel Finos, Livio |
| author_facet | De Santis, Riccardo Goeman, Jelle J. Hemerik, Jesse Davenport, Samuel Finos, Livio |
| contents | Generalized linear models usually assume a common dispersion parameter, an assumption that is seldom true in practice. Consequently, standard parametric methods may suffer appreciable loss of type I error control. As an alternative, we present a semi-parametric group-invariance method based on sign flipping of score contributions. Our method requires only the correct specification of the mean model, but is robust against any misspecification of the variance. We present tests for single as well as multiple regression coefficients. The test is asymptotically valid but shows excellent performance in small samples. We illustrate the method using RNA sequencing count data, for which it is difficult to model the overdispersion correctly. The method is available in the R library flipscores. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_13918 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Inference in generalized linear models with robustness to misspecified variances De Santis, Riccardo Goeman, Jelle J. Hemerik, Jesse Davenport, Samuel Finos, Livio Methodology Statistics Theory Generalized linear models usually assume a common dispersion parameter, an assumption that is seldom true in practice. Consequently, standard parametric methods may suffer appreciable loss of type I error control. As an alternative, we present a semi-parametric group-invariance method based on sign flipping of score contributions. Our method requires only the correct specification of the mean model, but is robust against any misspecification of the variance. We present tests for single as well as multiple regression coefficients. The test is asymptotically valid but shows excellent performance in small samples. We illustrate the method using RNA sequencing count data, for which it is difficult to model the overdispersion correctly. The method is available in the R library flipscores. |
| title | Inference in generalized linear models with robustness to misspecified variances |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2209.13918 |