The behavior of generalized relative complexity measures is studied for assessment of structural dependence in a random vector. A related optimality criterion to sampling network design, which provides a flexible extension of mutual information based methods previously introduced, is formulated. Aspects related to practical implementation and conceptual issues regarding the meaning and potential use of this new approach are discussed. Numerical examples are used for illustration.
(2014). Dependence assessment based on generalized relative complexity measures: application to sampling network design [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/31675
Dependence assessment based on generalized relative complexity measures: application to sampling network design
2014-01-01
Abstract
The behavior of generalized relative complexity measures is studied for assessment of structural dependence in a random vector. A related optimality criterion to sampling network design, which provides a flexible extension of mutual information based methods previously introduced, is formulated. Aspects related to practical implementation and conceptual issues regarding the meaning and potential use of this new approach are discussed. Numerical examples are used for illustration.| File | Dimensione del file | Formato | |
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