In densely-populated countries and in particular in large metropolitan areas, the presence of so much human activity causes all sorts of negative externalities, for example traffic noise disturbance. These externalities call for corrective measures by the government. Economists have developed a...

In densely-populated countries and in particular in large metropolitan areas, the presence of so much human activity causes all sorts of negative externalities, for example traffic noise disturbance. These externalities call for corrective measures by the government. Economists have developed a...

In densely-populated countries and in particular in large metropolitan areas, the presence of so much human activity causes all sorts of negative externalities, for example traffic noise disturbance. These externalities call for corrective measures by the government. Economists have developed a...

In densely-populated countries and in particular in large metropolitan areas, the presence of so much human activity causes all sorts of negative externalities, for example traffic noise disturbance. These externalities call for corrective measures by the government. Economists have developed a...

In practical applications related to, for instance, machine learning, data mining and pattern recognition, one is commonly dealing with noisy data lying near some low-dimensional manifold. A well-established tool for extracting the intrinsically low-dimensional structure from such data is...

The global push towards sustainability has led to increased interest in alternative power sources other than coal and fossil fuels. One of these sustainable sources is to harness energy from the wind through wind turbines. However, a significant hindrance preventing the widespread use of wind...

Academic and applied researchers in economics have, in the last 10 years, become increasingly interested in the topic of chaotic dynamics. In this paper we undertake non-linear dynamical analysis of one representative time series taken from financial markets, namely the Standard and Poor's (S&P)...

In this article, we propose a new estimation methodology to deal with PCA for high-dimension, low-sample-size (HDLSS) data. We first show that HDLSS datasets have different geometric representations depending on whether a ρ-mixing-type dependency appears in variables or not. When the...