Evaluating the impact of climate change on stream flow: integrating GCM, hydraulic modelling and functional data analysis

Ghumman, Abdul Razzaq; Ateeq-ur-Rauf,; Alodah, Abdullah; Haider, Husnain; Shafiquzzaman, Md.

doi:10.1007/s12517-020-05881-y

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…The desire to interpret many curvilinear data together has increased the requirements for functional data analysis methods, and with the development of these methods, it is aimed to reveal the underlying structures of functional data called curves or surfaces. With the development of functional equivalents of multivariate statistical analysis techniques, functional data analysis (FDA) techniques have found a wide range of applications such as financial data (Wang et al, 2021), medical data (Ullah and Finch, 2013), climate change (Ghumman et al, 2020), air quality (Martinez Torres et al, 2020), management science (Dass and Shropshire, 2012), pandemics (Tang et al, 2020), etc. A detailed review of applications of functional data analysis can be found in Ullah and Finch (2013).…”

Section: Introductionmentioning

confidence: 99%

Outlier detection based on the functional coefficient of variation

Kocakoç¹,

Keser²

2023

SiTns

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The coefficient of the variation function is a useful descriptive statistic, especially when comparing the variability of more than two curve groups, even when they have significantly different mean curves. Since the coefficient of variation function is the ratio of the mean and standard deviation functions, its particular property is that it shows the acceleration more explicitly than the standard deviation function. The aim of the study is twofold: to show that the functional coefficient of variation is more sensitive to abrupt changes than the functional standard deviation and to propose the utilisation of the functional coefficient of variation as an outlier detection tool. Several simulation trials have shown that the coefficient of the variation function allows the effects of outliers to be seen explicitly.

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