3,741 research outputs found
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.
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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
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