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

Jing Luo, Haoyu Wei, Xiaoyu Lei and Jiaxin Guo

Papers from arXiv.org

Abstract: For the differential privacy under the sub-Gamma noise, we derive the asymptotic properties of a class of network models with binary values with a general link function. In this paper, 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 asymptotic results.

Date: 2021-11, Revised 2023-11
New Economics Papers: this item is included in nep-ecm, nep-net and nep-ore
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