Confidence intervals for expected moments algorithm flood quantile estimates

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Log Pearson type 3 quantile estimators with regional skew information and low outlier adjustments (2004, methodology): “EMA flood quantile estimators were developed by Cohn et al. [2001] .”
Bayesian flood frequency analysis in the light of model and parameter uncertainties (2012, methodology): “In the methods, uncertainties are mainly characterized by the approximate confidence intervals (Stedinger 1983; Chowdhury and Stedinger 1991; Stedinger et al. 1993; Ashkar and Ouarda 1998; Whitley and Hromadka 1999; Cohn et al. 2001).”
Bayesian MCMC flood frequency analysis with historical information (2005, methodology): “A drawback of the MLE approach is that it does not always work well, such as when fitting a Log-Pearson Type 3 distribution (Cohn et al., 2001).”
Comparisons of two moments‐based estimators that utilize historical and paleoflood data for the log Pearson type III distribution (2003, methodology): “The ERL is defined as [Cohn et al., 1997] ERLT s; hð Þ ¼ s MSE X̂T s; h ¼ 0h i MSE X̂T s; h;Xoh i ð18Þ The ERL is defined as the number of years of systematic data that would produce the same MSE as the given combination of historical and systematic data combined [Cohn, 1984].”