Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data

51 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Non-stationary extreme value analysis in a changing climate (2014, background): “A variety of studies apply the GEV to analyze extremes (Katz 2013; Towler et al. 2010; AghaKouchak and Nasrollahi 2010; Beniston et al. 2007; El Adlouni et al. 2007; Villarini et al. 2009; Kharin and Zwiers 2005; Zhang et al. 2001; Smith 2001; Gumbel 1942).”
Non-Stationary Frequency Analysis of Extreme Water Level: Application of Annual Maximum Series and Peak-over Threshold Approaches (2017, methodology): “With the assumptions that the extreme water levels are independent (probability distribution parameters to be estimated come from independent and identically distributed observations), and that the data are stationary (driving forces that affect extreme data do not change in future), the generalized”
Changes in concurrent monthly precipitation and temperature extremes (2013, methodology): “While extreme values of precipitation and temperature often are addressed independently by employing univariate statistical methods (Cooley et al 2007, Katz 2010, AghaKouchak and Nasrollahi 2010, Zhang et al 2011, AghaKouchak et al 2010), analyses of concurrence of climate extremes are scarce, and c”