Analyzing the coexistence of DSRC and Wi-Fi networks using the Poisson line Cox process

2021 ◽  
pp. 101518
Author(s):  
Abdel Karim Ajami ◽  
Hussein Ammar ◽  
Hassan Artail
Keyword(s):  
Cox Process

scholarly journals Bayesian log‐Gaussian Cox process regression: applications to meta‐analysis of neuroimaging working memory studies

2018 ◽  
Vol 68 (1) ◽  
pp. 217-234 ◽  
Author(s):  
Pantelis Samartsidis ◽  
Claudia R. Eickhoff ◽  
Simon B. Eickhoff ◽  
Tor D. Wager ◽  
Lisa Feldman Barrett ◽  
...  
Keyword(s):  
Working Memory ◽  
Meta Analysis ◽  
Memory Studies ◽  
Cox Process ◽  
Log Gaussian Cox Process

scholarly journals The effects of changing meteorological parameters on fatal aortic catastrophes

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Brigitta Szilágyi ◽  
Márton Berczeli ◽  
Attila Lovas ◽  
Zoltán Oláh ◽  
Klára Törő ◽  
...  
Keyword(s):  
Atmospheric Pressure ◽  
Process Model ◽  
Meteorological Parameters ◽  
Main Tool ◽  
Environmental Parameters ◽  
Aortic Aneurysms ◽  
Occurrence Rate ◽  
Causal Connection ◽  
Cox Process ◽  
The Relationship

Abstract Background Over the span of the last decade, medical research has been increasingly putting greater emphasis on the study of meteorological parameters due to their connection to cardiovascular diseases. The main goal of this study was to explore the relationship between fatal aortic catastrophes and changes in atmospheric pressure and temperature. Methods We used a Cox process model to quantify the effects of environmental factors on sudden deaths resulting from aortic catastrophes. We used transfer entropy to draw conclusion about the causal connection between mortality and meteorological parameters. Our main tool was a computer program which we developed earlier in order to evaluate the relationship between pulmonary embolism mortality and weather on data sets comprised of aortic aneurysm (AA) and acute aortic dissection (AAD) cases, where one of these two medical conditions had led to fatal rupture of the aorta. Our source for these cases were the autopsy databases of Semmelweis University, from the time period of 1994 to 2014. We have examined 160 aneurysm and 130 dissection cases in relation to changes in meteorological parameters. The algorythm implemented in our program is based on a non-parametric a Cox process model. It is capable of splitting slowly varying unknown global trends from fluctuations potentially caused by weather. Furthermore, it allows us to explore complex non-linear interactions between meteorological parameters and mortality. Results Model measures the relative growth of the expected number of events on the nth day caused by the deviation of environmental parameters from its mean value. The connection between ruptured aortic aneurysms (rAA) and changes in atmospheric pressure is more significant than their connection with mean daily temperatures. With an increase in atmospheric pressure, the rate of rAA mortality also increased. The effects of meteorological parameters were weaker for deaths resulting from acute aortic dissections (AAD), although low mean daily temperatures increased the intensity of occurrence for AAD-related deaths. Conclusion The occurrence rate of fatal aortic catastrophes showed a slight dependence on the two examined parameters within our groups.

scholarly journals Thinning spatial point processes into Poisson processes

2010 ◽  
Vol 42 (02) ◽  
pp. 347-358 ◽  
Author(s):  
Jesper Møller ◽  
Frederic Paik Schoenberg
Keyword(s):  
Point Process ◽  
Process Model ◽  
Goodness Of Fit ◽  
Point Processes ◽  
Death Process ◽  
Cox Process ◽  
Birth And Death Process ◽  
Conditional Intensity ◽  
Spatial Point Processes ◽  
Spatial Point

In this paper we describe methods for randomly thinning certain classes of spatial point processes. In the case of a Markov point process, the proposed method involves a dependent thinning of a spatial birth-and-death process, where clans of ancestors associated with the original points are identified, and where we simulate backwards and forwards in order to obtain the thinned process. In the case of a Cox process, a simple independent thinning technique is proposed. In both cases, the thinning results in a Poisson process if and only if the true Papangelou conditional intensity is used, and, thus, can be used as a graphical exploratory tool for inspecting the goodness-of-fit of a spatial point process model. Several examples, including clustered and inhibitive point processes, are considered.

scholarly journals The Effects of Changing Meteorological Parameters on Fatal Aortic Catastrophes

2020 ◽  
Author(s):  
Brigitta Szilágyi ◽  
Márton Berczeli ◽  
Attila Lovas ◽  
Zoltán Oláh ◽  
Klára Törő ◽  
...  
Keyword(s):  
Atmospheric Pressure ◽  
Process Model ◽  
Meteorological Parameters ◽  
Main Tool ◽  
Environmental Parameters ◽  
Aortic Aneurysms ◽  
Occurrence Rate ◽  
Causal Connection ◽  
Cox Process ◽  
The Relationship

Abstract Introduction: Over the spam of the last decade, medical research has been increasingly putting greater emphasis on the study of meteorological parameters due to their connection to cardiovascular diseases. The main goal of this study was to explore the relationship between fatal aortic catastrophes and changes in atmospheric pressure and temperature. Methods: We used a Cox process model to quantify the effects of environmental factors on sudden deaths resulting from aortic catastrophes. We used transfer entropy to draw conclusion about the causal connection between mortality and meteorological parameters. Our main tool is a computer program which we developed earlier in order to evaluate the relationship between pulmonary embolism mortality and the weather on data sets comprised of aortic aneurysm (AA) and acute aortic dissection (AAD) cases, where one of these two medical conditions had led to the fatal rupture of the aorta. Our source for these cases were the autopsy databases of Semmelweis University, from the time period of 1994 to 2014. We have examined 160 aneurysm and 130 dissection cases in relation to changes in meteorological parameters. The algorithm implemented in our program is based on a non-parametric a Cox process model. It is capable to split slowly varying unknown global trends from fluctuations potentially caused by weather. Furthermore, it allows us to explore complex non-linear interactions between meteorological parameters and mortality. Results: The model measures the relative growth of the expected number of events on the n th day caused by the deviation of environmental parameters from its mean value. The connection between ruptured aortic aneurysms (rAA) and changes in atmospheric pressure is more significant than their connection with mean daily temperatures. With the increase in atmospheric pressure, the rate of rAA mortality also increased. The effects of meteorological parameters were weaker for deaths resulting from acute aortic dissections (AAD), although low mean daily temperatures increased the intensity of occurrence for AAD-related deaths. Conclusion: The occurrence rate of fatal aortic catastrophes showed a slight dependence on the two examined parameters within our groups.

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