The iterated score regression estimation algorithm for PCA-based missing data with high correlation.

ABSTRACT: To handle principal component analysis (PCA)-based missing data with high correlation, we propose a novel imputation algorithm to impute missing values, called iterated score regression. The procedure is first to draw into a transformation matrix, which puts missing values and observed values into two data blocks, and then by using the data blocks, the score matrix, and PCA model to construct the related regression equations. The estimation update at the iteration is highlighted. We examine the sensitivity of the proposed algorithm, including the effects of standard deviations, correlation coefficients, missing proportions, variable numbers, and sample sizes with different intervals of the standard deviations and correlation coefficients. To compare some existing algorithms, we suggest the

SUBMITTER: Guo G 

PROVIDER: S-EPMC11914595 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature