Modeling Extreme Stochastic Variations using the Maximum Order Statistics of Convoluted Distributions

Modeling extreme stochastic phenomena associated with catastrophic temperatures, heat waves, earthquakes and destructive floods is an aspect of proactive mitigation risk. Hydrologists, reliability engineers, meteorologist researchers among other stakeholders are faced the challenges providing adequate model for fitting real life datasets from natural hazardous occurrences in our environment. Convoluted distributions (CD) generalized value (GEV) distribution r- largest order statistics (r-LOS) have been some prominent existing techniques modeling events. This study explored properties convoluted as alternative procedure analyzing maximum aim obtaining superior fit compared to techniques. The new called MAXOS-G employed potential Maximum Order Statistics (MAXOS) flexibilities where G taken beWeibull-Exponential Pareto (WEP) New Kumaraswamy-Weibull (NKwei) distributions. WEP (MAXOS-WEP) NKwei (MAXOS-NKwei) was derived applied three consisting annual flood discharges, precipitation one-day rainfall. Some MAXOS-WEP investigated including moment, mean, variance, skewness, kurtosis. Characterization by L-moment presented coefficient L-variation, L-skewness L-kurtosis were derived. results application using R-software justified importance this MAXOS-NKwei models provide goodness-of-fit than better previously proposed datasets.