Estimating missing values in China's official socioeconomic statistics using progressive spatiotemporal Bayesian hierarchical modeling.

ABSTRACT: Due to a large number of missing values, both spatially and temporally, China has not published a complete official socioeconomic statistics dataset at the county level, which is the country's basic scale of official statistics data collection. We developed a procedure to impute the missing values under the Bayesian hierarchical modeling framework. The procedure incorporates two novelties. First, it takes into account spatial autocorrelations and temporal trends for those easier-to-impute variables with small missing percentages. Second, it further uses the first-step complete variables as covariate information to improve the modeling of more-difficult-to-impute variables with large missing percentages. We applied this progressive spatiotemporal (PST) method to China's official socioeconom

SUBMITTER: Song C 

PROVIDER: S-EPMC6030081 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature