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多元高维线性模型的信噪比推断AI 解读

We study inference for the fraction of total response variation explained by multivariate high-dimensional linear models. Two moment equations yield explicit estimates without fitting the regression coefficients. We establish asymptotically valid confidence intervals for fixed- and random-effects models, allowing the response dimension to grow with the sample size. The theory describes how response dependence affects precision and provides a correction for unequal noise…

高维随机效应模型信噪比估计的误设定分析AI 解读

Estimation of signal-to-noise ratios and residual variances in high-dimensional linear models has various important applications, including heritability estimation in bioinformatics. One widely used estimator is the Gaussian random-effects maximum likelihood estimator (MLE), based on the likelihood of the homogeneous Gaussian random-effects model in which both the regression coefficients and the noise variables are assumed to be i.i.d. Gaussian. This paper studies the…