Geodesic
Estimation of the Inclusive Development Index Based on the REL-PCANet Neural Network Model
Čebyševskij sbornik, Tome 21 (2020) no. 2, pp. 190-206.

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In 2018, at the World Economic Forum in Davos it was presented a new countries’ economic performance metric named the Inclusive Development Index (IDI) composed of 12 indicators. The new metric implies that countries might need to realize structural reforms for improving both economic expansion and social inclusion performance. That is why, it is vital for the IDI calculation method to have strong statistical and mathematical basis, so that results are accurate and transparent for public purposes. In the current work, we propose a novel approach for the IDI estimation — the Ranking Relative Principal Component Attributes Network Model (REL-PCANet). The model is based on RELARM and RankNet principles and combines elements of PCA, techniques applied in image recognition and learning to rank mechanisms. Also, we define a new approach for estimation of target probabilities matrix $T_{Rnet}$ to reflect dynamic changes in countries’ inclusive development. Empirical study proved that REL-PCANet ensures reliable and robust scores and rankings, thus is recommended for practical implementation.
Keywords: deep relative attributes, Inclusive Development Index, RankNet, Relative PCA attributes rating model, World Economic Forum. 
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A. A. Irmatov; E. A. Irmatova. Estimation of the Inclusive Development Index Based on the REL-PCANet Neural Network Model. Čebyševskij sbornik, Tome 21 (2020) no. 2, pp. 190-206. http://geodesic.mathdoc.fr/item/CHEB_2020_21_2_a14/

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