000195951 001__ 195951
000195951 005__ 20190206111628.0
000195951 037__ $$aPOST_TALK 000195951 245__$$aSynthesizing Data on Agents and Their Associations: A Simulation and Graph Theoretic Approach
000195951 269__ $$a2013 000195951 260__$$c2013
000195951 336__ $$aTalks 000195951 520__$$aData on the entire population is almost never publicly available. Moreover, there is an alarming trend of discontinuing the exercise of conducting full Census in many countries (Belgium, Switzerland, etc.). In this context, population synthesis techniques have been developed for policy analysis and forecasting. Currently, the focus is on treating synthesis as a fitting problem. For instance, Iterative Proportional Fitting (IPF) and Combinatorial Optimization based techniques. The key shortcomings of fitting based procedures include: a) synthesis of only one weighting scheme, while there can be many solutions b) due to cloning rather than true synthesis of the population, losing the heterogeneity that may not have been captured in the microdata c) over reliance on the accuracy of the data to determine the cloning weights d) poor scalability and convergence with respect to the increase in number of attributes of the synthesized agents. In order to overcome these shortcomings, we propose a Markov Chain Monte Carlo (MCMC) simulation based approach. Partial views of the joint distribution of agent¹s attributes that are available from various data sources can be used to simulate draws from the original distribution. The problem of association of different types of agents (person-households) is then treated as a maximum weight problem of a bipartite graph. The real population from Swiss census is used to compare the performance of simulation based synthesis with the standard IPF. The standard root mean square error statistics indicated that even the worst case simulation based synthesis (SRMSE=0.35) outperformed the best case IPF synthesis (SRMSE=0.64).
000195951 700__ $$0245136$$g207159$$aFarooq, Bilal 000195951 700__$$aBierlaire, Michel$$g118332$$0240563
000195951 7112_ $$dMay 31, 2013$$cLausanne$$aEighth Workshop on Discrete Choice Models 000195951 909C0$$xU11418$$0252123$$pTRANSP-OR
000195951 909CO $$ppresentation$$pENAC$$ooai:infoscience.tind.io:195951 000195951 937__$$aEPFL-TALK-195951
000195951 970__ $$aTALK-FARO_DCA2013/TRANSP-OR 000195951 973__$$aEPFL
000195951 980__ aTALK