Approximate confidence and tolerance limits for the discrete Pareto distribution for characterizing extremes in count data

2018 ◽  
Vol 73 (1) ◽  
pp. 4-21 ◽  
Author(s):  
D. S. Young ◽  
M. Naghizadeh Qomi ◽  
A. Kiapour
Keyword(s):  
Count Data ◽  
Pareto Distribution ◽  
Tolerance Limits ◽  
Discrete Pareto Distribution

scholarly journals Drawing transmission graphs for COVID-19 in the perspective of network science

2020 ◽  
Vol 148 ◽  
Author(s):  
N. Gürsakal ◽  
B. Batmaz ◽  
G. Aktuna
Keyword(s):  
Probability Distribution ◽  
Network Science ◽  
Pareto Distribution ◽  
Transmission Probability ◽  
Transmission Network ◽  
Pareto Principle ◽  
Infected People ◽  
Discrete Pareto Distribution ◽  
Local Transmission

Abstract When we consider a probability distribution about how many COVID-19-infected people will transmit the disease, two points become important. First, there could be super-spreaders in these distributions/networks and second, the Pareto principle could be valid in these distributions/networks regarding estimation that 20% of cases were responsible for 80% of local transmission. When we accept that these two points are valid, the distribution of transmission becomes a discrete Pareto distribution, which is a kind of power law. Having such a transmission distribution, then we can simulate COVID-19 networks and find super-spreaders using the centricity measurements in these networks. In this research, in the first we transformed a transmission distribution of statistics and epidemiology into a transmission network of network science and second we try to determine who the super-spreaders are by using this network and eigenvalue centrality measure. We underline that determination of transmission probability distribution is a very important point in the analysis of the epidemic and determining the precautions to be taken.

scholarly journals A general stochastic model for bivariate episodes driven by a gamma sequence

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Charles K. Amponsah ◽  
Tomasz J. Kozubowski ◽  
Anna K. Panorska
Keyword(s):  
Stochastic Model ◽  
Joint Distribution ◽  
Pareto Distribution ◽  
Integral Transforms ◽  
Bivariate Distribution ◽  
Conditional Distributions ◽  
General Stochastic Model ◽  
Heavy Tailed ◽  
Discrete Pareto Distribution ◽  
Special Case

AbstractWe propose a new stochastic model describing the joint distribution of (X,N), where N is a counting variable while X is the sum of N independent gamma random variables. We present the main properties of this general model, which include marginal and conditional distributions, integral transforms, moments and parameter estimation. We also discuss in more detail a special case where N has a heavy tailed discrete Pareto distribution. An example from finance illustrates the modeling potential of this new mixed bivariate distribution.

Tolerance limits on order statistics in future samples coming from the Pareto distribution

2016 ◽  
Vol 50 (6) ◽  
pp. 423-431 ◽  
Author(s):  
N. A. Nechval ◽  
K. N. Nechval ◽  
S. P. Prisyazhnyuk ◽  
V. F. Strelchonok
Keyword(s):  
Order Statistics ◽  
Pareto Distribution ◽  
Tolerance Limits

scholarly journals Drawing transmission graphs for COVID-19 in the perspective of network science

2020 ◽  
Author(s):  
Necmi Gursakal ◽  
Bulent Batmaz ◽  
Gamze Aktuna
Keyword(s):  
Probability Distribution ◽  
Power Law ◽  
Network Science ◽  
Pareto Distribution ◽  
Transmission Probability ◽  
Transmission Network ◽  
Pareto Principle ◽  
Infected People ◽  
Discrete Pareto Distribution

When we consider a probability distribution about how many COVID-19 infected people will transmit the disease, two points become important. First, there should be super-spreaders in these distributions/networks and secondly, the Pareto principle should be valid in these distributions/networks. When we accept that these two points are valid, the distribution of transmission becomes a discrete Pareto distribution, which is a kind of power law. Having such a transmission distribution, then we can simulate COVID-19 networks and find super-spreaders using the centricity measurements in these networks. In this research, in the first we transformed a transmission distribution of statistics and epidemiology into a transmission network of network science and secondly we try to determine who the super-spreaders are by using this network and eigenvalue centrality measure. We underline that determination of transmission probability distribution is a very important point in the analysis of the epidemic and determining the precautions to be taken.

Analysis of power law assumptions for short-period comets and Kuiper bodies

1999 ◽  
Vol 173 ◽  
pp. 289-293 ◽  
Author(s):  
J.R. Donnison ◽  
L.I. Pettit
Keyword(s):  
Power Law ◽  
Pareto Distribution ◽  
Limited Data ◽  
Short Period ◽  
Probability Plots ◽  
Exponential Probability

AbstractA Pareto distribution was used to model the magnitude data for short-period comets up to 1988. It was found using exponential probability plots that the brightness did not vary with period and that the cut-off point previously adopted can be supported statistically. Examination of the diameters of Trans-Neptunian bodies showed that a power law does not adequately fit the limited data available.

Regression Analysis of Count Data

1998 ◽  
Author(s):  
A. Colin Cameron ◽  
Pravin K. Trivedi
Keyword(s):  
Regression Analysis ◽  
Count Data

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data
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