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通过系数调整改进并显式化代理变量分析AI 解读

Unobserved environmental, demographic and technical factors canadversely affect the estimation and testing of the effects ofprimary variables. Surrogate variable analysis, proposed to tacklethis problem, has been widely used in genomic studies. To estimatehidden factors that are correlated with the primary variables,surrogate variable analysis performs principal component analysiseither on a subset of features or on all features, but weightingeach differently. However,…

病例对照关联研究中次要性状的统一分析AI 解读

It has been repeatedly shown that in case-control association studies, analysis of a secondary trait which ignores the original sampling scheme can produce highly biased risk estimates. Although a number of approaches have been proposed to properly analyze secondary traits, most approaches fail to reproduce the marginal logistic model assumed for the original case-control trait and/or do not allow for interaction between secondary trait and genotype marker on primary disease…

高维情形下主成分得分的收敛与预测AI 解读

A number of settings arise in which it is of interest to predict Principal Component (PC) scores for new observations using data from an initial sample. In this paper, we demonstrate that naive approaches to PC score prediction can be substantially biased towards 0 in the analysis of large matrices. This phenomenon is largely related to known inconsistency results for sample eigenvalues and eigenvectors as both dimensions of the matrix increase. For the spiked eigenvalue…