Identication and Estimation of Nonsymmetric Determinantal Point Processes
Determinantal point processes (DPPs) provide tractable distributions for nite random sets, but the usual symmetric-kernel specication imposes nonpositive pairwise dependence. Statistical research on nonsymmetric DPPs, which can accommodate positive dependence, remains limited. We study identication and estimation for this class. First, we establish the equivalence of two sucient conditions for a nonsymmetric kernel to dene a valid DPP. Second, we develop the identication theory for nonsymmetric DPPs. Third, we develop cardinality-truncated maximum-likelihood estimation and estimation by averaged stochastic gradient descent. Under suitable identication and regularity conditions, we establi...
| Authors | Christian Gouriéroux, Yang Lu |
|---|---|
| Primary category | stat.ML |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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