Asymptotic in a class of network models with an increasing sub-Gamma degree sequence

Jing Luo, Haoyu Wei, Xiaoyu Lei and Jiaxin Guo

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 9, 2507-2532

Abstract: For differential privacy under sub-Gamma noise, we derive the asymptotic properties of a class of network models with binary values with a general link function. In this article, we release the degree sequences of the binary networks under a general noisy mechanism, with the discrete Laplace mechanism as a special case. We establish the asymptotic result, including both consistency and asymptotically normality, of the parameter estimator when the number of parameters goes to infinity in a class of network models. Simulations and a real-data example are provided to illustrate the asymptotic results.

Date: 2025
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DOI: 10.1080/03610926.2024.2370915

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