Abstract

The aim of this chapter is to estimate unknown parameters of inverse Weibull (IW) distribution using eight different estimation methods: maximum likelihood (ML), least squares (LS), weighted least squares (WLS), percentile (PC), maximum product of spacing (MPS), probability weighted moments (PWM), Cramer–von Mises (CM), and Anderson-Darling (AD). The performances of these estimation methods are compared via an extensive Monte Carlo simulation study. Their robustness properties are also investigated. At the end of the study, two real data sets are analyzed for illustration and comparison purposes.

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