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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.14529 |
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| _version_ | 1866913848994299904 |
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| author | Gouriéroux, Christian Lu, Yang |
| author_facet | Gouriéroux, Christian Lu, Yang |
| contents | The Determinantal Point Process (DPP) is a parameterized model for multivariate binary variables, characterized by a correlation kernel matrix. This paper proposes a closed form estimator of this kernel, which is particularly easy to implement and can also be used as a starting value of learning algorithms for maximum likelihood estimation. We prove the consistency and asymptotic normality of our estimator, as well as its large deviation properties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14529 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A simple estimator of the correlation kernel matrix of a determinantal point process Gouriéroux, Christian Lu, Yang Machine Learning 62G30 The Determinantal Point Process (DPP) is a parameterized model for multivariate binary variables, characterized by a correlation kernel matrix. This paper proposes a closed form estimator of this kernel, which is particularly easy to implement and can also be used as a starting value of learning algorithms for maximum likelihood estimation. We prove the consistency and asymptotic normality of our estimator, as well as its large deviation properties. |
| title | A simple estimator of the correlation kernel matrix of a determinantal point process |
| topic | Machine Learning 62G30 |
| url | https://arxiv.org/abs/2505.14529 |