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

• Tác giả: Jiaxin Guo, Xiaoyu Lei, Jing Luo, Haoyu Wei
• Ngôn ngữ: eng
• Ký hiệu phân loại: 006.32 Neural nets (Neural networks)
• Thông tin xuất bản: 2021
• Bộ sưu tập: Metadata
• ID: 168126

Tóm tắt

Comment: arXiv admin note: text overlap with arXiv:2002.12733 by other authorsFor 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.