Generalized maximum‐likelihood generalized extreme‐value quantile estimators for hydrologic data

By Eduardo Sávio Passos Rodrigues Martins, Jery R. Stedinger. 2000

666 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/40e16f88fdff44457d9343d774b6dae7b18ff4e0

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Citations in the corpus

A note on the applicability of log-Gumbel and log-logistic probability distributions in hydrological analyses: I. Known pdf (2002, methodology): “Various investigators have used the concept of L-statistics recently (e.g. Adamowski, 2000; Martins & Stedinger, 2000).”
A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis (2018, methodology): “For example, for precipitation, the shape parameter is usually less the 0.5 and positive so we could apply pðnÞ Nð0; 0:3Þ (Renard, 2011) or the so-called ‘‘geophysical prior’’ (a shifted beta distribution; Martins & Stedinger, 2010).”
Use of a Gaussian copula for multivariate extreme value analysis: Some case studies in hydrology (2007, result): “This prior distribution can be compared with the Martins and Stedinger [42] geophysical prior, which is less variable and is entirely included in the interval [-0.5; Because almost non-informative priors are used, the posterior distribution will be mostly influenced by the likelihood, thus making…”
A Bayesian hierarchical approach to regional frequency analysis (2011, neutral): “3 is specified: This is similar to the ‘‘geophysical prior’’ used by Martins and Stedinger [2000]. Prior specification is further discussed in section 5.”
Generalized maximum likelihood estimators for the nonstationary generalized extreme value model (2007, methodology): “[24] The GML parameter estimator corresponds to the mode of the posterior distribution, which is not explicitly known, and can be computed by numerical methods [Martins and Stedinger, 2000].”
A hierarchical Bayesian GEV model for improving local and regional flood quantile estimates (2016, methodology): “We refer the reader to the works of Gaume et al. (2010), Haddad et al. (2012), Kwon et al. (2008), Liang et al. (2011), Lima and Lall (2010), Martins and Stedinger (2000), Parent and Bernier (2003), Reis and Stedinger (2005), Renard (2011), Renard et al. (2006), Renard and Lall (2014), Viglione et…”
Statistics of extremes in hydrology (2002, background): “Similarly, Martins and Stedinger [66] constrained the shape parameter through a Bayesian prior distribution whose support has an upper bound of c 1⁄4 1=2 (i.”
Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data (2010, background): “…2002; Nguyen et al. 2002; Segal et al. 2002; Crisci et al. 2008; Shane and Lynn 1964; Todorovic and Zelenhasic 1970; Madsen et al. 1997a, b; Alila 1999; Fowler and Kilsby 2003; Koutsoyiannis 2004; Semmler and Jacob 2004; Martins and Stedinger 2000; Morrison and Smith 2002; Glaves and Waylen 1997).”
An application of Bayesian analysis and Markov chain Monte Carlo methods to the estimation of a regional trend in annual maxima (2006, result): “This prior distribution can be compared with the Martins and Stedinger (2000) geophysical prior, which is less variable and is entirely included in the interval [-0.”
On the emergence of rainfall extremes from ordinary events (2016, methodology): “We estimate GEV parameters using the most efficient and most commonly used techniques: maximum likelihood (ML) [Coles, 2001; Martins and Stedinger, 2000; Gençay et al., 2001], L-moments [Hosking et al., 1985; Hosking, 1990], the peak over threshold approach [Davison and Smith, 1990], and mixed…”
Statistical Modelling of Spatial Extremes (2012, methodology): “Owing to difficulties in estimating the shape parameter, these authors used a penalty due to Martins and Stedinger (2000) to ensure that |ξ| 1/2.”
Bayesian estimation of rainfall intensity-duration-frequency relationships (2015, methodology): “Taken in this context, a beta prior for c, with support on ½ 0:5;0:5 and mean 0.1 seems realistic and not overly informative, a choice which was recommended by Martins and Stedinger (2000).”
Geostatistics of Dependent and Asymptotically Independent Extremes (2013, background): “Thus, moment- or likelihood-based inference may perform poorly, and alternative approaches, such as probability-weighted moments (Hosking et al. 1985; Hosking and Wallis 1987) or penalised likelihood (Martins and Stedinger 2000), have been promoted for use in simple settings.”