Adaptive censoring for large-scale regressions

Albeit being in the big data era, a significant percentage of data accrued can be overlooked while maintaining reasonable quality of statistical inference at affordable complexity. By capitalizing on data redundancy, interval censoring is leveraged here to cope with the scarcity of resources needed for data exchanging, storing, and processing. By appropriately modifying least-squares regression, first- and second-order algorithms with complementary strengths that operate on censored data are developed for large-scale regressions. Theoretical analysis and simulated tests corroborate their efficacy relative to contemporary competing alternatives.