A simple estimator of the correlation kernel matrix of a determinantal point process

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.

@article{gouriéroux2025_2505.14529,
  title={ A simple estimator of the correlation kernel matrix of a determinantal point process },
  author={ Christian Gouriéroux and Yang Lu },
  journal={arXiv preprint arXiv:2505.14529},
  year={ 2025 }
}