Big data and precision
Big data and precision
复制标题
大数据与精准
作者：
D. Cox
所谓的大数据可能具有复杂的结构，特别是意味着通过应用标准统计程序获得的精度估计可能会产生误导，即使参数本身的点估计可能相当令人满意。虽然这种可能性最好在每种特殊情况的背景下进行探讨，但在这里我们概述了大型系统中误差增加的相当一般的表示，并探讨了对回归系数估计的可能影响。讨论提出了与时间序列理论中短期依赖性和长期依赖性之间的区别大致平行的问题。
So-called big data are likely to have complex structure, in particular implying that estimates of precision obtained by applying standard statistical procedures are likely to be misleading, even if the point estimates of parameters themselves may be reasonably satisfactory. While this possibility is best explored in the context of each special case, here we outline a fairly general representation of the accretion of error in large systems and explore the possible implications for the estimation of regression coefficients. The discussion raises issues broadly parallel to the distinction between short-range and long-range dependence in time series theory.