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Modern statistical analysis often encounters datasets with large sizes. Forthese datasets, conventional estimation methods can hardly be used immediatelybecause practitioners often suffer from limited computational resources. Inmost cases, they do not have powerful computational resources (e.g., Hadoop orSpark). How to practically analyze large datasets with limited computationalresources then becomes a problem of great importance. To solve this problem, wepropose here a novel subsampling-based method with jackknifing. The key idea isto treat the whole sample data as if they were the population. Then, multiplesubsamples with greatly reduced sizes are obtained by the method of simplerandom sampling with replacement. It is remarkable that we do not recommendsampling methods without replacement because this would incur a significantcost for data processing on the hard drive. Such cost does not exist if thedata are processed in memory. Because subsampled data have relatively smallsizes, they can be comfortably read into computer memory as a whole and thenprocessed easily. Based on subsampled datasets, jackknife-debiased estimatorscan be obtained for the target parameter. The resulting estimators arestatistically consistent, with an extremely small bias. Finally, thejackknife-debiased estimators from different subsamples are averaged togetherto form the final estimator. We theoretically show that the final estimator isconsistent and asymptotically normal. Its asymptotic statistical efficiency canbe as good as that of the whole sample estimator under very mild conditions.The proposed method is simple enough to be easily implemented on most practicalcomputer systems and thus should have very wide applicability.

Subsampling and Jackknifing: A Practically Convenient Solution for Large Data Analysis with Limited Computational Resources