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Suppose we are given two independent strings of data from a known finite alphabet. We are interested in testing the null hypothesis that both the strings were drawn from the same distribution, assuming that the samples within each string are mutually indep ...
The present study proposes a validation of a mathematical index Q able to identify optimal geographic places for economic activities, solely based on the location variable. This research work takes its roots in the 1970s with the statistical analysis of sp ...
Interpolation is the means by which a continuously defined model is fit to discrete data samples. When the data samples are exempt of noise, it seems desirable to build the model by fitting them exactly. In medical imaging, where quality is of paramount im ...
Walking is the focus of increasing interest. However, the phenomenon of people who spontaneously walk great distances has not been investigated. Here, we analyse quantitative data from the Swiss transport micro-census (MRMT2010), containing information fro ...
Location-based social networks, in addition to revealing users' online social network, also informs users' actual movements in the offline physical world. Due to this, they have recently been used in large-scale mobility and urban studies. In this paper, u ...
The present study proposes a validation of a mathematical index Q able to identify optimal geographic places for economic activities, solely based on the location variable. This research work takes its roots in the 1970s with the statistical analysis of sp ...
We study the problem of parametric model-fitting in a finite alphabet setting. We characterize the weak convergence of the goodness-of-fit statistic with respect to an exponential family when the observations are drawn from some alternate distribution. We ...
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Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on ne ...
Generalized Linear Models (GLMs) are an increasingly popular framework for modeling neural spike trains. They have been linked to the theory of stochastic point processes and researchers have used this relation to assess goodness-of-fit using methods from ...
Statistical models of neural activity are at the core of the field of modern computational neuroscience. The activity of single neurons has been modeled to successfully explain dependencies of neural dynamics to its own spiking history, to external stimuli ...
