Title
Statistical tests based on Laplace and Hankel transforms, and their application in change point detection: doctoral disertation
Creator
Lukić, Žikica G., 1996-
CONOR:
129658377
Copyright date
2024
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Autorstvo-Nekomercijalno-Bez prerade 3.0 Srbija (CC BY-NC-ND 3.0)
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Language
Serbian
Cobiss-ID
Theses Type
Doktorska disertacija
description
Datum odbrane: 05.05.2025.
Other responsibilities
Academic Expertise
Prirodno-matematičke nauke
Academic Title
-
University
Univerzitet u Beogradu
Faculty
Matematički fakultet
Alternative title
Statistički testovi zasnovani na Laplasovim i Hankelovim transformacijama, i njihova primena u otkrivanju promena režima
Publisher
[Ž. Lukić]
Format
98 str.
description
Mathematics-Probability and Statistics/Matematika-Verovatnoća i statistika
Abstract (en)
The main goal of this dissertation is twofold. In the first part, two novel twosample
tests for matrix data are presented. The theoretical properties of these novel tests are
investigated in the context of testing orthogonal invariance in distribution, while the empirical
values are presented in other cases. The tests are not distribution-free under H0. Therefore,
their quality is investigated through a power study by implementing the warp-speed bootstrap
algorithm. The novel tests are applied to multiple cases of real data, primarily originating in the
field of finance. These tests are the first of their kind for two-sample tests of positive definite
symmetric matrix distributions and are based on Laplace and Hankel transforms.
The second part of this dissertation addresses problems related to data segmentation (or
change point detection). Two novel classes of univariate tests for offline data segmentation
are outlined, and their theoretical properties are studied. The powers are estimated using the
permutation bootstrap algorithm, and the novel tests are shown to have higher test powers than
the well-known tests based on the characteristic function. The location of the change point
is estimated, and the novel tests are empirically demonstrated to possess greater precision.
These tests are applied to two distinct datasets from meteorology and macroeconomics, further
emphasizing their applicability in real-case scenarios.
Moreover, the two-sample test based on the Hankel transform is modified to address change
point problems. The asymptotic properties of this novel test are derived. A power study is
presented, demonstrating the quality of the novel test in small-sample scenarios. The novel
test is applied to financial data, emphasizing the practical applicability of this approach. This
represents the first test for change point inference based on integral transforms for matrix
data.
Abstract (sr)
Glavni cilj ove disertacije je dvojak. U prvom delu su predstavljeni dvouzorački
testovi za matrične raspodele. Teorijska svojstva novih testova su predstavljena u slučaju testiranja
ortogonalne invarijatnosti u raspodeli, dok su u ostalim slučajevima date empirijske
vrednosti. Novi testovi nisu slobodni od raspodele pri nultoj hipotezi. Zbog toga su empirijske
moći testova dobijene uz pomoć ubrzanog butstrepa. Novi testovi su primenjeni na stvarne podatke,
uglavnom iz oblasti finansija. Ovi testovi su prvi takvi dvouzorački testovi za simetrične
pozitivno definitne matrice zasnovani na Laplasovim i Hankelovim transformacijama.
Drugi deo ove disertacije posvećen je problemu otkrivanja tačke promene režima (ili segmentiranja
podataka). Nove klase jednodimenzionih testova za otkrivanje aposteriori promene
režima su predstavljene i njihova teorijska svojstva su istražena. Empirijske moći testova su
ocenjene korištenjem permutacijskog butstrepa. Novi testovi imaju veće moći testova u odnosu
na konkurentske testove. Prikazan je i empirijski kvalitet ocene tačke promene i primećeno je
da novi testovi imaju veću preciznost u odnosu na konkurentske testove. Ovi testovi su primenjeni
na različite podatke iz meteorologije i makroekonomije, čime je pokazana njihova
praktična primena.
Dodatno, dovuzorački test zasnovan na Hankelovoj transformaciji je modifikovan da bi
otkrivao tačke promene režima. Asimptotska svojstva novog testa su izvedena. Određene su i
empirijske moći testova, čime je pokazan kvalitet novog testa u slučaju uzoraka malog obima.
Novi test je primenjen na finansijske podatke, što je dodatno prikazalo praktičnu primenu
ovoga testa. Ovaj test je prvi takav test promene režima zasnovan na integralnim transformacijama
za matrične podatke.
Authors Key words
Hankelova transformacija, Laplasova transformacija, matrične raspodele,
Višartova raspodela, necentralna Višartova raspodela, podaci vezani za kriptovalute, stabilnost
finansijskih tržišta, dvouzorački testovi, otkrivanje promena režima
Authors Key words
Hankel transform, Laplace transform, matrix distributions, Wishart distribution,
noncentral Wishart distribution, cryptocurrency data, stability of financial markets, twosample
tests, change point inference
Classification
517.4/.5(043.3)
Type
Tekst
Abstract (en)
The main goal of this dissertation is twofold. In the first part, two novel twosample
tests for matrix data are presented. The theoretical properties of these novel tests are
investigated in the context of testing orthogonal invariance in distribution, while the empirical
values are presented in other cases. The tests are not distribution-free under H0. Therefore,
their quality is investigated through a power study by implementing the warp-speed bootstrap
algorithm. The novel tests are applied to multiple cases of real data, primarily originating in the
field of finance. These tests are the first of their kind for two-sample tests of positive definite
symmetric matrix distributions and are based on Laplace and Hankel transforms.
The second part of this dissertation addresses problems related to data segmentation (or
change point detection). Two novel classes of univariate tests for offline data segmentation
are outlined, and their theoretical properties are studied. The powers are estimated using the
permutation bootstrap algorithm, and the novel tests are shown to have higher test powers than
the well-known tests based on the characteristic function. The location of the change point
is estimated, and the novel tests are empirically demonstrated to possess greater precision.
These tests are applied to two distinct datasets from meteorology and macroeconomics, further
emphasizing their applicability in real-case scenarios.
Moreover, the two-sample test based on the Hankel transform is modified to address change
point problems. The asymptotic properties of this novel test are derived. A power study is
presented, demonstrating the quality of the novel test in small-sample scenarios. The novel
test is applied to financial data, emphasizing the practical applicability of this approach. This
represents the first test for change point inference based on integral transforms for matrix
data.
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