Data envelopment analysis vs. principal component analysis: an illustrative study of economic performance of Chinese cities

Article Abstract:

Data envelopment analysis and principal component analysis are found to be effective tools in evaluating decision making units as they provide consistent and complimentary results. The two techniques on aggregating multiple inputs and multiple outputs were used to measure the performance of Chinese cities and the two techniques provide effective and reliable data as nonparametric statistical tests show.

Evaluation, Data envelopment analysis, Principal components analysis

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Error structure identification condition in maximum likelihood nonmetric multidimensional scaling

Article Abstract:

The elimination of a unjustifiable error models for similarity input data has been found to increase the utility of maximum likelihood estimation for multidimensional scaling (MLMDS). Identification constraints producing correct asymptotic confidence regions are also proposed which effectively address the identification condition problem associated with the use of metric and nonmetric MLMDS.

Maximum likelihood estimates (Statistics), Multidimensional scaling, Maximum likelihood (Statistics)

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