06
3012405004818
Ciaco scrl
onixsuitesupport@onixsuite.com
20200815
eng
COM.ONIXSUITE.9782874632358
03
02
i6doc
01
SKU
83086
02
287463235X
03
9782874632358
15
9782874632358
10
BC
<TitleType>01</TitleType>
<TitleText>Thèses UCL</TitleText>
Numéro 205
Thèses de la Faculté des sciences
205
<TitleType>01</TitleType>
<TitleText>Heavy tailed functional time series</TitleText>
01
GCOI
28001100908580
1
A01
Thomas Meinguet
Meinguet, Thomas
Thomas
Meinguet
<p>Thomas Meinguet graduated in Mathematics in 2004 from the Université catholique de Louvain (Belgium) with highest honors. After a stay at Cornell University in the United States (NY), he completed his Ph.D. thesis in Louvain-la-Neuve in 2010.</p>
1
01
eng
02
eng
172
00
172
03
26
1
SCI000000
29
2012
3051
SCIENCES FONDAMENTALES
01
06
01
<P>The goal of this thesis is to treat the temporal tail dependence and the cross-sectional tail dependence of heavy tailed functional time series. Functional time series are aimed at modelling spatio-temporal phenomena; for instance rain, temperature, pollution on a given geographical area, with temporally dependent observations. Heavy tails mean that the series can exhibit much higher spikes than with Gaussian distributions for instance. In such cases, second moments cannot be assumed to exist, violating the basic assumption in standard functional data analysis based on the sequence of autocovariance operators. As for random variables, regular variation provides the mathematical backbone for a coherent theory of extreme values. The main tools introduced in this thesis for a regularly varying functional time series are its tail process and its spectral process. These objects capture all the aspects of the probability distribution of extreme values jointly over time and space. The development of the tail and spectral process for heavy tailed functional time series is followed by three theoretical applications. The first application is a characterization of a variety of indices and objects describing the extremal behavior of the series: the extremal index, tail dependence coefficients, the extremogram and the point process of extremes. The second is the computation of an explicit expression of the tail and spectral processes for heavy tailed linear functional time series. The third and final application is the introduction and the study of a model for the spatio-temporal dependence for functional time series called maxima of moving maxima of continuous functions (CM3 processes), with the development of an estimation method.</p>
03
<P>The goal of this thesis is to treat the temporal tail dependence and the cross-sectional tail dependence of heavy tailed functional time series. Functional time series are aimed at modelling spatio-temporal phenomena; for instance rain, temperature, pollution on a given geographical area, with temporally dependent observations. Heavy tails mean that the series can exhibit much higher spikes than with Gaussian distributions for instance. In such cases, second moments cannot be assumed to exist, violating the basic assumption in standard functional data analysis based on the sequence of autocovariance operators. As for random variables, regular variation provides the mathematical backbone for a coherent theory of extreme values. The main tools introduced in this thesis for a regularly varying functional time series are its tail process and its spectral process. These objects capture all the aspects of the probability distribution of extreme values jointly over time and space. The development of the tail and spectral process for heavy tailed functional time series is followed by three theoretical applications. The first application is a characterization of a variety of indices and objects describing the extremal behavior of the series: the extremal index, tail dependence coefficients, the extremogram and the point process of extremes. The second is the computation of an explicit expression of the tail and spectral processes for heavy tailed linear functional time series. The third and final application is the introduction and the study of a model for the spatio-temporal dependence for functional time series called maxima of moving maxima of continuous functions (CM3 processes), with the development of an estimation method.</p>
02
The goal of this thesis is to treat the temporal tail dependence and the cross-sectional tail dependence of heavy tailed functional time...
04
01
http://www.i6doc.com/resources/titles/28001100908580/images/5e5dd00d770ef3e9154a4257edcb80b8/THUMBNAIL/9782874632358.jpg
17
03
01
https://www.i6doc.com/resources/publishers/35.jpg
18
09
01
https://www.i6doc.com/resources/publishers/73.png
38
https://www.i6doc.com/en/book/?GCOI=28001100908580
06
3052405007518
Presses universitaires de Louvain
01
06
3052405007518
Presses universitaires de Louvain
04
20100801
434
2010
02
WORLD
01
9.45
in
02
6.30
in
03
0.96
in
08
10.02
oz
01
24
cm
02
16
cm
03
0.96
cm
08
284
gr
06
3012405004818
CIACO - DUC
33
www.i6doc.com
http://www.i6doc.com
03
WORLD 01 2
20
1
02
00
02
02
STD
02
19.00
EUR
R
6.00
17.92
1.08