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Thus, the mean time between peaks, including the residence time or mean time before the very first peak, is the inverse of the frequency of exceedance N −1 (y max). If the number of peaks exceeding y max grows as a Poisson process, then the probability that at time t there has not yet been any peak exceeding y max is e −N(y max)t. [6] Its ...
Buffered probability of exceedance (bPOE) is a function of a random variable used in statistics and risk management, including financial risk. The bPOE is the probability of a tail with known mean value . The figure shows the bPOE at threshold (marked in red) as the blue shaded area.
Under some other settings, TVaR is the conditional expectation of loss above a given value, whereas the expected shortfall is the product of this value with the probability of it occurring. [3] The former definition may not be a coherent risk measure in general, however it is coherent if the underlying distribution is continuous. [ 4 ]
Cumulative frequency is also called frequency of non-exceedance. Cumulative frequency analysis is performed to obtain insight into how often a certain phenomenon (feature) is below a certain value. This may help in describing or explaining a situation in which the phenomenon is involved, or in planning interventions, for example in flood ...
An estimate of the uncertainty in the first and second case can be obtained with the binomial probability distribution using for example the probability of exceedance Pe (i.e. the chance that the event X is larger than a reference value Xr of X) and the probability of non-exceedance Pn (i.e. the chance that the event X is smaller than or equal ...
Diffusion-limited aggregation (DLA) is the process whereby particles undergoing a random walk due to Brownian motion cluster together to form aggregates of such particles. . This theory, proposed by T.A. Witten Jr. and L.M. Sander in 1981, [1] is applicable to aggregation in any system where diffusion is the primary means of transport in the sy
Gumbel has also shown that the estimator r ⁄ (n+1) for the probability of an event — where r is the rank number of the observed value in the data series and n is the total number of observations — is an unbiased estimator of the cumulative probability around the mode of the distribution.
In probability theory and statistics, the generalized extreme value (GEV) distribution [2] is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions.