Stochastic Loewner evolution driven by Levy processes

Standard stochastic Loewner evolution (SLE) is driven by a continuous Brownian motion, which then produces a continuous fractal trace. If jumps are added to the driving function, the trace branches. We consider a generalized SLE driven by a superposition of a Brownian motion and a stable Levy process. The situation is defined by the usual SLE parameter, $\kappa$, as well as $\alpha$ which defines the shape of the stable Levy distribution. The resulting behavior is characterized by two descriptors: $p$, the probability that the trace self-intersects, and $\tilde{p}$, the probability that it will approach arbitrarily close to doing so. Using Dynkin's formula, these descriptors are shown to change qualitatively and singularly at critical values of $\kappa$ and $\alpha$. It is reasonable to call such changes ``phase transitions''. These transitions occur as $\kappa$ passes through four (a well-known result) and as $\alpha$ passes through one (a new result). Numerical simulations are then used to explore the associated touching and near-touching events.Comment: Published version, minor typos corrected, added reference

On the use of a four-parameter kappa distribution in regional frequency analysis

New developments are presented enabling the using a four-parameter kappa distribution with the widely used regional goodness-of-fit methods as part of an index flood regional frequency analysis based on the method of L-moments. The framework was successfully applied to 564 pooling groups and was found to significantly improve the probabilistic description of British flood flow compared to existing procedures. Based on results from an extensive data analysis it is argued that the successful application of the kappa distribution renders the use of the traditional three-parameter distributions such as the generalized extreme value (GEV) and generalized logistic (GLO) distributions obsolete, except for large and relatively dry catchments. The importance of these findings is discussed in terms of the sensitivity of design floods to distribution choice

Optimization of a skewed logistic distribution with respect to the Kolmogorov-Smirnov test

The four-parameter kappa distribution (Hosking 1994) was analyzed with respect to the various possible shapes of the probability density function. The general form for the cumulative distribution function when both h and k are non-zero is: F(x) = { 1 - h [ 1 - k ( x - ξ ) / α ]1/k }1/h. The parameters h and k work together to define the function\u27s shape, ξ affects location, and α is the scale parameter. A method of selecting parameters to minimize the Kolmogorov-Smirnov test statistic, D, was developed. The technique was first described for the logistic distribution, which is the special case of the kappa distribution with k = 0 and h = -1. Then the more general case, k = 0 and h ≠ 0, was further explored as a possibility for expanding the optimization technique. The optimization method was shown to provide the parameters h, ξ, and α such that the Kolmogorov-Smirnov test statistic, D, was minimized. This optimization was applied to several example data sets and found to produce distributions that fit the empirical data much better than the normal or lognormal distribution functions. The results have potential applications in describing the distributions of many types of real data, including, but not limited to, weather, hydrologic and other environmental data. Matching empirical data to an invertible probability distribution makes it convenient to simulate random data that follow closely the characteristics of the natural data. Preliminary inquiry suggested that the technique might be expanded to allow non-zero values of k. This would improve the shape flexibility slightly and produce slightly better fits to empirical data

The Partial L-Moment of the Four Kappa Distribution

Statistical analysis of extreme events such as flood events is often carried out to predict large return period events. The behaviour of extreme events not only involves heavy-tailed distributions but also skewed distributions, similar to the four-parameter Kappa distribution (K4D). In general, this covers many extreme distributions such as the generalized logistic distribution (GLD), the generalized extreme value distribution (GEV), the generalized Pareto distribution (GPD), and so on. To utilize these distributions, we have to estimate parameters accurately. There are many parameter estimation methods, for example, Method of Moments, Maximum Likelihood Estimator, L-Moments, or partial L-Moments. Nowadays, no researchers have applied the partial L-Moments method to estimate the parameters of K4D. Therefore, the objective of this paper is to derive the partial L-Moments (PL-Moments) for K4D, namely the PL-Moments of the K4D in order to estimate hydrological extremes from censored data. The findings of this paper are formulas of parameter estimation for K4D based on the PL-Moments approach. We have derived the Partial Probability-Weighted Moments (PPWMs) of the K4D (β'r) and derive the estimation of parameters when separated by shape parameters (k,h) conditions i.e., case k>-1 and h>0, case k>-1 and h=0 and case -1<k<-1/h and h<0. Finally, we expect that the parameter estimate for K4D from this formula will help to make accurate forecasts. Doi: 10.28991/ESJ-2023-07-04-06 Full Text: PD