本文针对高维数据统计推断中的变量选择问题进行研究。当数据维度很高时,进行统计推断可能导致计算复杂度增加和误差累加,因此提出了一种高效的基于统计推断的变量选择方法。该方法以一种更为搜索性的方法对变量做出选择,重点突出了筛选出相关变量,并利用特定算法对数据进行降维,以优化推算矩阵的计算过程。经过对结构化和非结构化高维数据的实验验证,发现所提出的方法能有效减少理论偏误,并提供稳健和高效的估计结果。同时,该方法对复杂模型结构的识别能力强,具有较好的稳健性和预测性。这项研究有助于在高维数据环境下提升统计推断的准确性和效率,为大量高维数据分析提供新的视角和工具。This paper focuses on the issue of variable selection in statistical inference for high-dimensional data. When data dimensionality is high, statistical inference may lead to increased computational complexity and accumulated errors. Therefore, an efficient variable selection method based on statistical inference is proposed. This method selects variables in a more exploratory manner, emphasizing the screening of relevant variables, and utilizes specific algorithms for dimensionality reduction to optimize the calculation process of the estimation matrix. Experimental validation on both structured and unstructured high-dimensional data reveals that the proposed method can effectively reduce theoretical biases and provide robust and efficient estimation results. Additionally, this method demonstrates strong identification capabilities for complex model structures, with good robustness and predictability. This research contributes to enhancing the accuracy and efficiency of statistical inference in high-dimensional data environments, providing new perspectives and tools for the analysis of large amounts of high-dimensional data.
