Online Program
Maximal, minimal sample coordination and associated bounds*Alina Matei, University of Neuchatel, SwitzerlandKeywords: Fréchet bounds, joint selection probability of two samples, linear programming. Sample coordination maximizes or minimizes the overlap of two or more samples selected from overlapping populations. It can be applied to designs with simultaneous or sequential sample selection. The degree of coordination is measured by the expected sample overlap, which is limited by theoretical bounds (absolute upper and lower bounds). Two types of bounds can be defined: on unit level and on marginal sampling designs' level. We consider the bounds on unit level, which depend on unit inclusion probabilities. If the expected overlap equals the absolute upper bound, the sample coordination is maximal; if the expected overlap equals the absolute lower bound, the sample coordination is minimal. It is possible to construct optimal sampling designs for given unit inclusion probabilities to realize maximal or minimal coordination. This approach was developed by Matei and Skinner (2009) and uses a combination of the iterative proportional fitting algorithm and the linear programming implementation to controlled sampling method. We study here the performance of this method using a real scenario survey based on Swiss municipality data set. Despite the computing facilities available nowadays, the problem can be prohibitively large even for moderately large population and sample size. The method is useful to solve moderatesized sample coordination problems.
