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High-Dimensional Data with Measurement Error

Herman Tesso · Georges Nguefack-Tsague首次提交 2026-05-28 · 修订 2026-05-28 · v1arXiv预印本

高维数据中的测量误差：惩罚回归方法综述与比较AI 解读

In many important statistical analyses, the number of covariates $p$ often exceeds the data size $n$ , a regime commonly referred to as high-dimensional. While considerable progress has been made in high-dimensional regression under the assumption of error-free covariates, real-world data frequently involve noisy or corrupted measurements. When left unaddressed, measurement errors can silently distort the analysis and mislead the conclusions. This paper reviews and evaluates…In many important statistical analyses, the number of covariates $p$ often exceeds the data size $n$, a regime commonly referred to as high-dimensional. While considerable progress has been made in high-dimensional regression under the assumption of error-free covariates, real-world data frequently involve noisy or corrupted measurements. When left unaddressed, measurement errors can silently distort the analysis and mislead the conclusions. This paper reviews and evaluates…

正则化与变量选择

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-12

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