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<p>The asymptotic normality of the fixed number of the parameter estimators in undirected weighted networks with an increasing differentially private sequence has been established recently. In this paper, we further derive the central limit theorem for linear combinations of all the parameter estimators with an increasing differentially private sequence for undirected finite weighted network. Simulation studies are provided to illustrate the asymptotic results.</p>

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Asymptotic Distribution in Undirected Finite Weighted Random Graphs with an Increasing Differentially Private Degree Sequence

  • 期刊论文

  • 发表日期：

    2025年11月13日

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  • Jing Luo,
  • Hong Qin

摘要

The asymptotic normality of the fixed number of the parameter estimators in undirected weighted networks with an increasing differentially private sequence has been established recently. In this paper, we further derive the central limit theorem for linear combinations of all the parameter estimators with an increasing differentially private sequence for undirected finite weighted network. Simulation studies are provided to illustrate the asymptotic results.