Functional extreme data analysis methods and its application to rainfall data
Functional data analysis is one of the new techniques to transform a discrete or continuous observation into a functional form. Conventional classical statistics methods now can be observed and analyzed through a curve for almost all types of data with neither distribution assumption nor goodn...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/77182/1/IPM%202018%2011%20IR.pdf |
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| Summary: | Functional data analysis is one of the new techniques to transform a discrete or continuous
observation into a functional form. Conventional classical statistics methods
now can be observed and analyzed through a curve for almost all types of data
with neither distribution assumption nor goodness of fit test that are necessary to be
followed. Literature reviews show that there is no study found for functional data
analysis application on extreme data which deals with maximum value in the data
set.
In this thesis, the study has extended the functional data analysis methodology to
cover on extreme data with several substitution methods have been introduced. Some
characteristics of functional extreme data analysis such as on environmental data are
explained. The tolerance bands for functional mean extreme data is proposed using
bootstrapping method by implementing the percentile computation in determining
the upper and lower limits of the mean function.
The performance of the functional extreme data analysis is carried out. The equal
and unequal space of time cases are considered to be implemented for the functional
extreme data. The study found that only data that consists a large number of extreme
data will be performed in functional extreme data for unequal space of time. Otherwise,
a small number of extreme data is suggested to use the equal space of time to
obtain a smooth curve. |
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