In machine learning and data science literature, clustering is the task of dividing the observations (data points) into several categories in such a way that data points falling into one group are being dissimilar than the data points falling to the other groups such that the variation within a...

Training a Multi-Layer Perceptron (MLP) to achieve a minimum level of MSE is akin to doing Non-Linear Regression (NLR). Therefore, we use available econometric theory and the corresponding tools in R. Only if certain assumptions about the error term in the Data Generating Process are in place,...

This study reassesses the resource–economic growth nexus by incorporating several channels. Advanced panel time series techniques are used to analyze panel time series data from 1980 to 2015 in 31 oil-rich countries. Results show that oil rent augments economic growth; thus, oil rent is...

This paper provides a Market Model which implies a dynamic for standardized CDS index tranche spreads, i.e. tranches which securitise CDS index series and dispose of predefined subordination. The central idea consists in defining the forward Fair Tranche Spread as a function of the numeraire...

The Long-Wave theories of Nikolai Kondratiev and others claim to find mathematic waves in economic and other social data which are at present in dispute. Currently the theory is considered outside the scope of mainstream economics under several rationales. Despite the lack of mainstream...

The paper discusses opportunities to utilize the series of micro-blogs as provided by the Twitter in observation of opinion dynamics. The spontaneity of tweets is more, as the service is attached more to the mobile communications. The extraction of information in the series of tweets is...

El objetivo de este artículo es mostrar algunos de los aspectos matemáticos que se encuentran detrás de los modelos realizados para describir fenómenos económicos y financieros.Nos concentraremos en particular en las dos herramientas de base que son elmovimiento Browniano y la integral...

Large networks call for approaches which allow to represent their relevant properties. One relevant approach can consider the decomposition of the defined networks in significant subgraphs as k-cores. So it is possible to identify the nodes which can have an important role on the network. In...

Selecting proper methods of data modelling is crucial in geosciences, as effective data visualization enables better understanding of complex geological phenomena: processes, structure and dynamics. Various approaches of the data analysis by R language include both traditional methods of linear...

The Regression Tree (RT) sorts the samples using a specific feature and finds the split point that produces the maximum variance reduction from a node to its children. Our key observation is that the best factor to use (in terms of MSE drop) is always the target itself, as this most clearly...