Inference for generalized additive mixed models via penalized marginal likelihood
Fuente:
arXiv
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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909577837019136 |
|---|---|
| author | Stringer, Alex |
| author_facet | Stringer, Alex |
| contents | The Laplace approximation is sometimes not sufficiently accurate for smoothing parameter estimation in generalized additive mixed models. A novel estimation strategy is proposed that solves this problem and leads to estimates exhibiting the correct statistical properties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inference for generalized additive mixed models via penalized marginal likelihood Stringer, Alex Methodology The Laplace approximation is sometimes not sufficiently accurate for smoothing parameter estimation in generalized additive mixed models. A novel estimation strategy is proposed that solves this problem and leads to estimates exhibiting the correct statistical properties. |
| title | Inference for generalized additive mixed models via penalized marginal likelihood |
| topic | Methodology |
| url | https://arxiv.org/abs/2501.13797 |