KNN imputation gives highest photo-z accuracy under ideal random missingness with complete training data, while SAITS is more robust for incomplete training sets and realistic mixed missingness patterns in CSST data.
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Joint real and redshift space analysis of CosmicFlows-4++ yields BAO scales of 132±8 h^{-1}Mpc (real) and 139±7 h^{-1}Mpc (redshift) at z=0.07 together with fσ8=0.344±0.105.
citing papers explorer
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Comparative analysis of missing data imputation methods for CSST survey: Impact on photometric redshift estimation performance
KNN imputation gives highest photo-z accuracy under ideal random missingness with complete training data, while SAITS is more robust for incomplete training sets and realistic mixed missingness patterns in CSST data.
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Measuring $f\sigma_8$ and BAO scale in the Local Universe: a joint real and redshift space analysis from CosmicFlows-4++
Joint real and redshift space analysis of CosmicFlows-4++ yields BAO scales of 132±8 h^{-1}Mpc (real) and 139±7 h^{-1}Mpc (redshift) at z=0.07 together with fσ8=0.344±0.105.