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Asymptotic behavior of critical primitive multi-type branching processes with immigration
Asymptotic behavior critical primitive multi-type branching processes immigration Probability
2012/5/2
Under natural assumptions a Feller type diffusion approximation is derived for critical multi-type branching processes with immigration when the offspring mean matrix is primitive (in other words, pos...
Patterns of Regional Travel Behavior: An Analysis of Japanese Hotel Reservation Data
Japanese Hotel Reservation Mixture of Poisson distributions EM-algorithm Parameter Estimation
2012/4/2
This study considers the availability of room opportunities collected from a Japanese hotel booking site. We empirically analyze the daily number of room opportunities for four areas. To determine the...
Random Walk on a Co-Compact Fuchsian Group: Behavior of the Green's Function at the Spectral Radius
hyperbolic group surface group randomwalk Green’s function Gromov boundary Martin boundary Ruelle operator theorem
2011/7/27
Abstract: It is proved that the Green's function of a symmetric finite range random walk on a co-compact Fuchsian group decays exponentially in distance at the radius of convergence R. It is also show...
Behavior of an Almost Semicontinuous Poisson Process on a Markov Chain Upon Attainment of A Level
Semicontinuous Poisson Process Markov Chain Probability
2011/7/10
Abstract: We consider the almost semi-continuous processes defined on a finite Markov chain. The representation of the moment generating functions for the absolute maximum after achievement positive l...
Joint Extremal Behavior of Hidden and Observable Time Series
tail chain time series joint extremal behavior ARCH
2011/7/3
Abstract: We analyze the joint extremal behavior of two real-valued processes (X_t) and (Y_t) which can be interpreted as an observable and an unobservable time series. Our analysis is motivated by th...
Delayed feedback causes non-Markovian behavior of neuronal firing statistics
Delayed feedback non-Markovian behavior neuronal firing statistics
2010/12/31
The instantaneous state of a neural network consists of both the degree of excitation of each neuron, the network is composed of, and positions of impulses in communication lines between neurons.