Particle swarm optimization based Liu-type estimator

In this study, a new method for the estimation of the shrinkage and biasing parameters of Liu-type estimator is proposed. Because k is kept constant and d is optimized in Liu’s method, a (k, d) pair is not guaranteed to be the optimal point in terms of the mean square error of the parameters. The optimum (k, d) pair that minimizes the mean square error, which is a function of the parameters k and d, should be estimated through a simultaneous optimization process rather than through a two-stage process. In this study, by utilizing a different objective function, the parameters k and d are optimized simultaneously with the particle swarm optimization technique.     

How to Reduce the Impact of Equivalence Scales on Poverty Measurement: Evidence from Turkey

The main aim of the paper is to contribute to the poverty measurement literature by demonstrating a method to reduce the impact of equivalence scales in poverty measurement. This is accomplished by choosing the most appropriate reference household type. The results showed that one adult household is certainly not suitable for being the reference household type. When one adult household is set as the reference, in the range of no equivalence scale and per capita equivalence scale, poverty head count ratio changes from 1 to 48 %, showing the huge effect of the equivalence scale choice. Also the analyses at household size level showed that one adult household type is not convenient to be the reference household type. On the other hand, no clear distinction could be made between central household types, but the importance of choosing a household type close to the center was demonstrated for Turkish data.