Statistical inference method under high-dimensional data and its theoretical discussion in accurate analysis

Traditional statistical inference methods face many challenges when dealing with high-dimensional data, such as high computational complexity, poor model interpretation and over-fitting. In order to meet these challenges, this paper systematically introduces modern statistical inference methods for high-dimensional data, including dimensionality reduction methods, sparse modeling, high-dimensional covariance estimation and hypothesis testing, and discusses the applicable scenarios and selection suggestions of these methods. Under the background of precise analysis, this paper emphasizes the importance of statistical inference methods for high-dimensional data. Accurate analysis requires accurate extraction of useful information from massive and complex data to support decision-making. Through the application example of sparse linear regression model in gene expression data analysis, this paper shows how to use high-dimensional statistical inference method to screen significant genes, construct gene co-expression network and conduct pathway enrichment analysis, thus revealing the role of genes in the pathogenesis of diseases and providing basis for personalized treatment programs. The research results of this paper show that the statistical inference method under high-dimensional data can effectively meet the challenges brought by high-dimensional data and play an important role in accurate analysis. By selecting and applying these methods reasonably, the accuracy and reliability of data analysis can be improved, which provides strong support for accurate decision-making in various fields.