Monte Carlo experiments on the detection of trends in extreme values

By Xuebin Zhang, F. Zwiers, Guilong Li. 2004

239 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f33c2aad305abb97c94f36be20d34869ea9f6850

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Citations in the corpus

Spatial and temporal patterns of dry spell lengths in the Iberian Peninsula for the second half of the twentieth century (2008, methodology): “According to Zhang et al. (2004) , the slope estimation should be based on the Kendall’s tau”
Plotting Positions in Extreme Value Analysis (2006, background): “Furthermore, it is crucial for the safety and economically optimized engineering of future communities to be able to estimate the changes in the frequency of various natural hazards with climatic change, and analyzing trends in the weather extremes (Zhang et al. 2004).”
Different ways to compute temperature return levels in the climate change context (2010, methodology): “Different ways of identifying trends in extremes have been tested in Zhang et al. (2004) and the study concluded that methods based on the modeling of trends in the parameters of the distribution of the extremes are the most powerful.”
Generalized maximum likelihood estimators for the nonstationary generalized extreme value model (2007, methodology): “Using Monte Carlo simulations, Zhang et al. [2004] compared several methods for detecting trends in the magnitude of extreme values.”
Non-stationary extreme value analysis in a changing climate (2014, background): “05, which is widely used in hydrological research (Zhang et al. 2004).”
Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan (2017, methodology): “In addition to schemes based on the annual maximum timevarying moment, r-largest, peaks-over-threshold, and point process models are the alternatives for non-stationary analysis (Zhang et al., 2004; Rootzén and Tajvidi, 1997; McNeil and Saladin, 2000; Katz et al., 2002; Khaliq et al., 2006; Alexandr”
Joint Bayesian model selection and parameter estimation of the generalized extreme value model with covariates using birth‐death Markov chain Monte Carlo (2009, methodology): “[4] The use of the models with covariates makes it possible to combine the effect of other variables to classical frequency analysis models [Zhang et al., 2004; Clarke, 2002].”
Trends in temperature and rainfall extremes in the Yellow River source region, China (2011, methodology): “Kunkel et al. 1999; Zhang et al. 2001) and outperforms the least-squares regression in computing the magnitude of linear trends when the sample size is large ( Zhang et al. 2004 ).”