The stationary case

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  • Knowledge of the Earth’s structure and dynamics calls for a multi-disciplinary study that makes use of the most advanced methods of Physics, Chemistry, Geology, Mathematics and Information Technology, in the framework, or in close collaboration with, the different branches of Earth Sciences such as Geology, Geophysics and Geodesy.

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  • Only statistical inference with stationary variables provides valid results. In simple words, this is because if variables are non-stationary then any correlation between the explanatory and the dependent variable could be due to the trending in both variables that is caused by a third variable not included in the model. We tested for the non-stationarity of the variables in our model formally with the help of Levin, Lin and Chu’s (2002) unit root test for panel data.

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  • Topology Control in Wireless Ad Hoc and Sensor Networks makes the case for topology control and provides an exhaustive coverage of TC techniques in wireless ad hoc and sensor networks, considering both stationary networks, to which most of the existing solutions are tailored, and mobile networks. The author introduces a new taxonomy of topology control and gives a full explication of the applications and challenges of this important topic.

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  • CHAPTER 21 ARMA Modeling of CTA Returns. In this chapter, we extend previous attempts to model hedge fund returns using ARMA models to the case of CTAs. We show that for the period 1996 to 2003, the return series of the largest CTAs are stationary and that ARMA models in certain cases provide adequate representation of the return series.

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  • The second question requires that we build an understanding of how the policy instrument effects production and productive efficiency. Our approach is similar to recent stochastic frontier analyses with panel data (Cornwell et al. (1990), Kumbhakar (1990)), which allow intercepts and some coefficients of the production function to vary between firms and over time.

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