|
|
Quantification of multivariate risk
Hilbert, Hynek ; Hlubinka, Daniel (advisor) ; Hudecová, Šárka (referee)
In the present work we study multivariate extreme value theory. Our main focus is on exceedances over linear thresholds. Smaller part is devoted to exce- edances over elliptical thresholds. We consider extreme values as those which belong to remote regions and investigate convergence of their distribution to the limit distribution. The regions are either halfspaces or ellipsoids. Working with halfspaces we distinguish between two setups: we either assume that the distribution of extreme values is directionally homogeneous and we let the halfspaces diverge in any direction, or we assume that there are some irre- gularities in the sample cloud which show us the fixed direction we should let the halfspaces drift out. In the first case there are three limit laws. The domains of attraction contain unimodal and rotund-exponential distributions. In the second case there exist a lot of limit laws without general form. The domains of attraction also fail to have common structure. The similar situation occurs for the exceedances over elliptical thresholds. The task here is to investigate convergence of the random vectors living in the complements of ellipsoids. For all, the limit distributions are determined by affine transformations and distribution of spectral measure. 1
|
|
|
Stochastic Evolution Equations
Čoupek, Petr ; Maslowski, Bohdan (advisor) ; Garrido-Atienza, María J. (referee) ; Hlubinka, Daniel (referee)
Stochastic Evolution Equations Petr Čoupek Doctoral Thesis Abstract Linear stochastic evolution equations with additive regular Volterra noise are studied in the thesis. Regular Volterra processes need not be Gaussian, Markov or semimartingales, but they admit a certain covariance structure instead. Particular examples cover the fractional Brownian motion of H > 1/2 and, in the non-Gaussian case, the Rosenblatt process. The solution is considered in the mild form, which is given by the variation of constants formula, and takes values either in a separable Hilbert space or the space Lp(D, µ) for large p. In the Hilbert-space setting, existence, space-time regularity and large-time behaviour of the solutions are studied. In the Lp setting, existence and regularity is studied, and in concrete cases of stochastic partial differential equations, the solution is shown to be a space-time continuous random field.
|
|
| |
|
| |
|
|
Unfair Ballots
Valášková, Zuzana ; Hlubinka, Daniel (advisor) ; Lachout, Petr (referee)
OBSAH Nazov prace: Nespravodlivc losovanie Antor: Zuzana Valaskova Katedra: Katedra pravdepodobnosti a matematicke statistiky Veduci bakalarskej prace: R.NDr.Daniel Hlnbinka, Ph.D. e-mail veduceho pracc: hluhu]k;i:'"Jkarlm.mif.cuni.cz Abstrakt: V predlozencj praci studujem otazku ncspravodliveho losovania, ktorn som sku- mala prostrednictvom silneho nastroja, testovania hypotez. Na zaciatku som sa zaoberala najdenim vhodnej nahodnej veliciny. ktora by iiam umoznila jedno- duchym sposobom rozrieait! povodnu otazkn spravodlivosti losovania. Zaoberala som sa otazkou randomizovanych a nerandomizovanych testov. V praci su okrein ineho nvedenc iri vzorove priklady s podrobnyrn riescnim. Vo vsetkych jc nvedena aplikacia randoinizovaneho i nerondomizovaneho testu. Prvy ilustruje prave take losovaiiie, u ktoreho neniame dostatocne dovody na pochybovanie o spravodlivosti celeho losovania, v drnhoni priklade je uvedeny typicky pri[)ad ncspravodliveho losovania a v l.rel'oTn je uvedeny pripa.fi. kedy t,(^sty nedavaju rovnake vyskxlky. Prilohu tvori zdrojovy text nnmerickych vypoctov v Mathernatice. Kmmve slova: losovanie, sf>rav(>dlivost'.tnst Title: Unfair Ballots Author: Zuzana Valaskova Department: Department of Probability and Mathematical Statistics Supervisor: RNDr.Daniel Illubinka, Ph.D. Supervisor's e-mail...
|
|
|
Itôův a Stratonovičův stochastický integrál
Voldán, Adam ; Hlubinka, Daniel (advisor) ; Dostál, Luboš (referee)
In this thesis the Ito stochastic integral and the Stratonovich stochastic integrals are studied. Their basic and some special properties are shown. Further the theory of the numerical solution of stochastic differential equations (SDE) is introduced. Using simple examples the properties of chosen numerical schemes are presented. Finally the Black-Scholes-Merton formula for pricing of European call option is sketched, and similar problems are numerically solved using the above presented algorithms.
|
|
| |
|
| |
|
|
Selected topics of random walks
Filipová, Anna ; Hlubinka, Daniel (advisor) ; Beneš, Viktor (referee)
The theme of this thesis are symmetric random walks. We define different types of paths and prove the reflection principle. Then, based on the paths, we define random walks. The thesis also deals with probabilities of returns to the origin and first returns to the origin, further with probabilities of number of changes of sign or returns to the origin up to a certain time. We also define the maximum of the random walk and the first passage through a certain point. In the second chapter, we solve several problems, which form the proofs of some theorems from the first chapter or complement the first chapter in a different way. For example, we prove geometrically that the number of paths of one type equals the number of paths of another type or we compute the probability that there occurs a certain number of changes of sign up to a given time.
|
|
|
Depth of variance matrices
Brabenec, Tomáš ; Nagy, Stanislav (advisor) ; Hlubinka, Daniel (referee)
The scatter halfspace depth is a quite recently established concept which extends the idea of the location halfspace depth for positive definite matrices. It provides an interest- ing insight into the problem of suitability quantification of a matrix for the description of the covariance structure of the multivariate distribution. The thesis focuses on the investigation of theoretical properties of the depth for both general and more specific probability distributions which can be used for data analysis. It turns out that the es- timators of scatter parameters based on the empirical scatter depth are quite effective even under relatively weak assumptions. These estimators are useful especially for dealing with a sample containing outliers or contaminating observations. 1
|
guest ::
