Abstract: Sampling survey is still an indispensable data acquisition and statistical inference method in the era of big data, but better value depends on the adaptation of sampling method to the real situation of big data. Among them, how to extract representative samples of research variables is the most concerned problem. A comprehensive score sampling method based on slice inverse regression is proposed to solve this problem. The slice inverse regression can integrate the dependent variable information into the independent variable. Firstly, slice inverse regression analysis is used on big data to improve its dimension reduction process. Then, the comprehensive score of each principal component is taken as the sampling probability. The results of data simulation analysis show that the proposed method has better sampling estimation effect compared with the sampling without implementation and simple random sampling estimation in the big data situation, and the better sampling estimation effect appears when the individual difference is large. Finally, the feasibility and effectiveness of this method are verified by the actual data.

Key words: big data, slice inverse regression, principal component analysis, comprehensive score, sampling estimation