Semi-parametric tail inference through probability-weighted moments

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Summary: For heavy-tailed models, and working with the sample of the $k$ largest observations, we present probability weighted moments (PWM) estimators for the first order tail parameters. Under regular variation conditions on the right-tail of the underlying distribution function $F$ we prove the consistency and asymptotic normality of these estimators. Their performance, for finite sample sizes, is illustrated through a small-scale Monte Carlo simulation.
Original languageUnknown
Pages (from-to)937-950
JournalJournal Of Statistical Planning And Inference
Issue number2
Publication statusPublished - 1 Jan 2011

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