The maximum \(L_q\)-likelihood method: an application to extreme quantile estimation in finance
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Publication:398802
DOI10.1007/s11009-007-9063-1zbMath1293.62064OpenAlexW2088094195MaRDI QIDQ398802
Sandra Paterlini, Davide Ferrari
Publication date: 15 August 2014
Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11009-007-9063-1
Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Parametric inference (62F99) Statistics of extreme values; tail inference (62G32)
Related Items
Maximum L\(q\)-likelihood estimation ⋮ Testing for the shape parameter of generalized extreme value distribution based on the \(L_q\)-likelihood ratio statistic ⋮ Maximum Lq-Likelihood Estimation for the parameters of Marshall-Olkin Extended Burr XII Distribution ⋮ Penalized Lq-likelihood estimators and variable selection in linear regression models ⋮ Modified maximum spacings method for generalized extreme value distribution and applications in real data analysis ⋮ Conditional maximum Lq-likelihood estimation for regression model with autoregressive error terms ⋮ On unbiased optimal \(L\)-statistics quantile estimators
Uses Software
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