scholarly journals Fractional extreme distributions

2020 ◽  
Vol 25 (0) ◽  
Author(s):  
Lotfi Boudabsa ◽  
Thomas Simon ◽  
Pierre Vallois
Keyword(s):  
Extreme Distributions

Questioning MLE for the estimation of environmental extreme distributions

2014 ◽  
Vol 92 ◽  
pp. 44-54 ◽  
Author(s):  
Franck Mazas ◽  
Philippe Garat ◽  
Luc Hamm
Keyword(s):  
Extreme Distributions ◽  
Environmental Extreme

Predictions of the aids epidemic in the U.K.: The use of the back projection method

1989 ◽  
Vol 325 (1226) ◽  
pp. 123-134 ◽  
Keyword(s):  
Incubation Period ◽  
Projection Method ◽  
Hiv Infections ◽  
Back Projection ◽  
Aids Epidemic ◽  
Period Distribution ◽  
Highly Sensitive ◽  
The Future ◽  
Extreme Distributions

The results of this analysis illustrate three points. First, that for predictions of AIDS cases four to five years into the future, the back projection method is largely insensitive to the assumption one makes for the incubation period distribution. The two extreme distributions considered represent the fast and slow extremes of incubation period distribution usually proposed; distributions that lie between these two give predictions within the range of predictions that the two generate. The estimated number of new HIV infections, however, is highly sensitive to the assumed incubation period distribution; prediction of AIDS cases in the long term will be similarly sensitive.

The quasi-stationary distribution of the closed endemic sis model

1996 ◽  
Vol 28 (3) ◽  
pp. 895-932 ◽  
Author(s):  
Ingemar Nåsell
Keyword(s):  
Stationary Distribution ◽  
Geometric Distribution ◽  
Threshold Value ◽  
Basic Reproduction Ratio ◽  
Sis Model ◽  
Reproduction Ratio ◽  
Time To Extinction ◽  
Extreme Distributions ◽  
Stochastic Sis Model ◽  
Quasi Stationary Distribution

The quasi-stationary distribution of the closed stochastic SIS model changes drastically as the basic reproduction ratio R0 passes the deterministic threshold value 1. Approximations are derived that describe these changes. The quasi-stationary distribution is approximated by a geometric distribution (discrete!) for R0 distinctly below 1 and by a normal distribution (continuous!) for R0 distinctly above 1. Uniformity of the approximation with respect to R0 allows one to study the transition between these two extreme distributions. We also study the time to extinction and the invasion and persistence thresholds of the model.

Spatiotemporal patterns of annual and seasonal precipitation extreme distributions across China and potential impact of tropical cyclones

2017 ◽  
Vol 37 (10) ◽  
pp. 3949-3962 ◽  
Author(s):  
Xihui Gu ◽  
Qiang Zhang ◽  
Vijay P. Singh ◽  
Lin Liu ◽  
Peijun Shi
Keyword(s):  
Tropical Cyclones ◽  
Precipitation Extreme ◽  
Spatiotemporal Patterns ◽  
Seasonal Precipitation ◽  
Potential Impact ◽  
Extreme Distributions

scholarly journals On the convergence of extreme distributions under power normalization

2008 ◽  
Vol 35 (2) ◽  
pp. 145-153
Author(s):  
E. M. Nigm
Keyword(s):  
Power Normalization ◽  
Extreme Distributions

EXPRESS: Extremity Bias in Online Reviews: The Role of Attrition

2021 ◽  
pp. 002224372110735
Author(s):  
Leif Brandes ◽  
David Godes ◽  
Dina Mayzlin
Keyword(s):  
Large Scale ◽  
Heavy Tails ◽  
Online Reviews ◽  
Empirical Observation ◽  
Online Ratings ◽  
Large Scale Field ◽  
Theoretical Mechanism ◽  
Differential Utility ◽  
Online Travel ◽  
Extreme Distributions

In a range of studies across platforms, online ratings have been shown to be characterized by distributions with disproportionately-heavy tails. We focus on understanding the underlying process that yields such “j-shaped” or “extreme” distributions. We propose a novel theoretical mechanism behind the emergence of “j-shaped” distributions: differential attrition, or the idea that potential reviewers with moderate experiences are more likely to leave the pool of active reviewers than potential reviewers with extreme experiences. We present an analytical model that integrates this mechanism with two extant mechanisms: differential utility and base rates. We show that while all three mechanisms can give rise to extreme distributions, only the utility-based and the attrition-based mechanisms can explain our empirical observation from a large-scale field experiment that an unincentivized solicitation email from an online travel platform reduces review extremity. Subsequent analyses provide clear empirical evidence for the existence of both differential attrition and differential utility.

The quasi-stationary distribution of the closed endemic sis model

1996 ◽  
Vol 28 (03) ◽  
pp. 895-932 ◽  
Author(s):  
Ingemar Nåsell
Keyword(s):  
Stationary Distribution ◽  
Geometric Distribution ◽  
Threshold Value ◽  
Basic Reproduction Ratio ◽  
Sis Model ◽  
Reproduction Ratio ◽  
Time To Extinction ◽  
Extreme Distributions ◽  
Stochastic Sis Model ◽  
Quasi Stationary Distribution

The quasi-stationary distribution of the closed stochastic SIS model changes drastically as the basic reproduction ratio R 0 passes the deterministic threshold value 1. Approximations are derived that describe these changes. The quasi-stationary distribution is approximated by a geometric distribution (discrete!) for R 0 distinctly below 1 and by a normal distribution (continuous!) for R 0 distinctly above 1. Uniformity of the approximation with respect to R 0 allows one to study the transition between these two extreme distributions. We also study the time to extinction and the invasion and persistence thresholds of the model.

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