李凡群, 杨桂元, 张孔生. 基于相邻选择的Ising模型的非凹惩罚估计[J]. 应用概率统计, 2019, 35(2): 165-177. DOI: 10.3969/j.issn.1001-4268.2019.02.005
引用本文: 李凡群, 杨桂元, 张孔生. 基于相邻选择的Ising模型的非凹惩罚估计[J]. 应用概率统计, 2019, 35(2): 165-177. DOI: 10.3969/j.issn.1001-4268.2019.02.005
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LI Fanqun, YANG Guiyuan, ZHANG Kongsheng. Non-Concave Penalized Estimation Based on the Neighborhood Selection Method for Ising Model[J]. Chinese Journal of Applied Probability and Statistics, 2019, 35(2): 165-177. DOI: 10.3969/j.issn.1001-4268.2019.02.005
Citation: LI Fanqun, YANG Guiyuan, ZHANG Kongsheng. Non-Concave Penalized Estimation Based on the Neighborhood Selection Method for Ising Model[J]. Chinese Journal of Applied Probability and Statistics, 2019, 35(2): 165-177. DOI: 10.3969/j.issn.1001-4268.2019.02.005
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基于相邻选择的Ising模型的非凹惩罚估计

Non-Concave Penalized Estimation Based on the Neighborhood Selection Method for Ising Model

    摘要
  • 摘要: 本文对Ising模型的局部条件似然施加非凹惩罚,得到相应参数的Oracle性和渐近正态性. 在一致的界下,得到了Ising模型的参数矩阵的符号相合性估计, 以及在矩阵L_1范数下估计的收敛速度. 随机模拟和实例分析表明,非凹惩罚估计的灵敏度普遍较高.

     

    Abstract: In this paper, we put non-concave penalty on the local conditional likelihood. We obtain the oracle property and asymptotic normal distribution property of the parameters in Ising model. With a union band, we obtain the sign consistence for the estimator of parameter matrix, and the convergence speed under the matrix L_1 norm. The results of the simulation studies and a real data analysis show that the non-concave penalized estimator has larger sensitivity.

     

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