Integration of survey data and big observational data for finite population inference using mass imputation

Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining a probability sample with big observational data. Unlike the usual imputation for missing data analysis, we create imputed values for the whole elements in the probability sample. Such mass imputation is attractive in the context of survey data integration (Kim and Rao, 2012). We extend mass imputation as a tool for data integration of survey data and big non-survey data. The mass imputation methods and their statistical properties are presented. The matching estimator of Rivers (2007) is also covered as a special case. Variance estimation with mass-imputed data is discussed. The simulation results demonstrate the proposed estimators outperform existing competitors in terms of robustness and efficiency

Predictive mean matching imputation in survey sampling

Predictive mean matching imputation is popular for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the predictive mean matching estimator of the population mean. For variance estimation, the conventional bootstrap inference for matching estimators with fixed matches has been shown to be invalid due to the nonsmoothness nature of the matching estimator. We propose asymptotically valid replication variance estimation. The key strategy is to construct replicates of the estimator directly based on linear terms, instead of individual records of variables. Extension to nearest neighbor imputation is also discussed. A simulation study confirms that the new procedure provides valid variance estimation.Comment: 20 pages, 0 figure, 1 tabl

Specialization, Information, and Growth: A Sequential Equilibrium Analysis

Pricing costs and information problems are introduced into a framework with consumer-producers, economies of specialization, and transaction costs to predict the endogenous and concurrent evolution in division of labor and in the information of organization acquired by society. The concurrent evolution generates endogenous growth based on the tradeoff between gains from information about the efficient pattern of division of labor, which can be acquired via experiments with various patterns of division of labor, and experimentation costs, which relate to the costs in discovering prices. The concept of Walras sequential equilibrium is developed to analyze the social learning process which is featured with uncertainties of the direction of the evolution as well as a certain trend of the evolution.Coevolution of specialization and information, adaptive decision, bounded rationality, sequential equilibrium, economic development.

Supporting Trajectory UDF Queries and Indexes on PostGIS

In this paper, we propose a system model for querying and indexing the GPS trajectory of moving objects on PostGIS/PostgreSQL. We developed moving object data types including MPoint(moving point), MDouble(moving double) for GPS trajectories. Also, various moving objects UDFs(user-defined functions) are implemented for moving objects queries. For efficient query processing, r-tree index is extended for trajectory, and pre-materialization techniques are proposed for fast UDF processing. Experimental results show that the pre-materialization techniques are about 1.2 times faster than naïve query processing using r-tree index