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Conditional score matching for high-dimensional partial graphical models

Author

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  • Fan, Xinyan
  • Zhang, Qingzhao
  • Ma, Shuangge
  • Fang, Kuangnan

Abstract

Network construction has been heavily exploited in multivariate data analysis. In many cases, connections between a large portion of variables are of minimal importance. As such, partial graphs have played an important role in network construction. Due to the existence of a multiplicative normalization constant, the existing construction approaches may bear high computational cost. To reduce the computational complexity, the conditional score matching for high-dimensional partial graphical models is proposed. This approach is uniquely advantageous by being not influenced by the multiplicative normalization constant. An effective computational algorithm is developed, and it is shown that the computational complexity of the proposed method is less than that of those in the literature. Statistical properties are established, and two extensions are explored to incorporate more information and accommodate more general distributions. A wide spectrum of simulations and the analysis of a breast cancer gene expression dataset demonstrate competitive performance of the proposed methods.

Suggested Citation

  • Fan, Xinyan & Zhang, Qingzhao & Ma, Shuangge & Fang, Kuangnan, 2021. "Conditional score matching for high-dimensional partial graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:csdana:v:153:y:2021:i:c:s0167947320301572
    DOI: 10.1016/j.csda.2020.107066
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