scholarly journals Fast multivariate empirical cumulative distribution function with connection to kernel density estimation

Author(s):  
Nicolas Langrené ◽  
Xavier Warin
2016 ◽  
Vol 61 (3) ◽  
pp. 489-496
Author(s):  
Aleksander Cianciara

Abstract The paper presents the results of research aimed at verifying the hypothesis that the Weibull distribution is an appropriate statistical distribution model of microseismicity emission characteristics, namely: energy of phenomena and inter-event time. It is understood that the emission under consideration is induced by the natural rock mass fracturing. Because the recorded emission contain noise, therefore, it is subjected to an appropriate filtering. The study has been conducted using the method of statistical verification of null hypothesis that the Weibull distribution fits the empirical cumulative distribution function. As the model describing the cumulative distribution function is given in an analytical form, its verification may be performed using the Kolmogorov-Smirnov goodness-of-fit test. Interpretations by means of probabilistic methods require specifying the correct model describing the statistical distribution of data. Because in these methods measurement data are not used directly, but their statistical distributions, e.g., in the method based on the hazard analysis, or in that that uses maximum value statistics.


2011 ◽  
Vol 55-57 ◽  
pp. 209-214
Author(s):  
Yu Ling Wang ◽  
Jing Wang

A new method, non-parametric kernel density, is used to research the distribution function of HangSeng index returns. The new method can not only depict the character of peak and fat tails of stock returns, but also capture the market risk better than normal distribution. Further more, more accurate conclusions are concluded.


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