TL-moments and L-moments estimation of the generalized logistic distribution

Abstract

The generalized logistic (GLO) distribution has been used widely in extreme value event evaluation and also popular in hydrological risk analysis. In estimating the high return period events, censoring the data from below might be advantageous since the small floods are less significant to large ones, so the used of small floods can sometimes be only a nuisance value. In this paper the method of trimmed L-moments with one smallest value were trimmed (TLMOM1) was introduced as an alternative ways in estimating the flood for higher return period. TLMOM1 has an ability to reduce undesirable influence of small sample might have compared to former TL-moments (TLMOM) and L-moments (LMOM) method. The main objective of this study is to derive the TLMOM1 for GLO distribution. The performance of TLMOM1 was compared with LMOM and TLMOM through Monte Carlo simulation and stream flows data over station in Terengganu, Malaysia. The result shows that in certain cases, TLMOM1 is a better option as compared to LMOM and TLMOM in modelling those series.