Yongjin Jin, Zhan Liu

【Abstract】

When sampling is done with big data, the construction of sampling frame is difficult in many cases, so that the sample belongs to non-probability sample, and it is difficult to apply the traditional inference theory of sampling to the non-probability sample. Therefore, under the background of big data it is a serious challenge to sampling survey to solve the statistical inference problems of non-probability sampling. The research proposes some basic ideas to solve the statistical inference problems of non-probability sampling. First, sampling methods such as the sample selection method based on sample matching and the method of link-tracing sampling can be considered, so that the obtained non-probability sample approximates to probability sample and then the statistical inference theory of probability sample can be used. Second, the construction and adjustment methods of weights based on pseudo design, models and propensity score can be considered to obtain the base weights similar to probability sample. Third, the estimation methods based on pseudo design, models and Bayesian hybrid probability can be considered. Finally, the sample selection method based on sample matching is taken as an example to discuss concrete solutions to the statistical inference problems of non-probability sampling.

【Keywords】

big data, non-probability sampling, statistical inference