Browsing by Title
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Chandrasekhar, S. (Chicago , 1943)[more][less]
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Chaichain, M.; Demichev, A. (Bristol , 2001)[more][less]
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Freund Thorsten Poschel, A. Jan (Springer , 2000)[more][less]
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Ratnarajah, N.; Simmons, A.; Hojjat, A. (London , 2013)[more][less]
URI: http://hdl.handle.net/123456789/3224 Files in this item: 1
Stochastic_Two_TensorFibre.pdf ( 605.0Kb ) -
Merkle, Ana (Beograd , 2023)[more][less]
Abstract: Many new developments in the filed of probability and statistics focus on finding causal connections between observed processes. This leads to considering dependence relations and investigating how the past influence the present and the future. The well known concept of Granger (1969) causality is closely related to the idea of local dependence introduced by Schweder (1970). Granger studied time series, while Schweder considered Markov chains. The concept was later extended to more general stochastic processes by Mykland (1986). All this concepts incorporate the time into consideration dependence. The dissertation consist of four chapters. New results are presented in the fourth chap- ter. The main aim of this doctoral dissertation is to determine di↵erent concepts of stochastic predictability using the well known tool of conditional independence. Follow Granger’s idea, relationships between family of sigma - algebras (filtrations) and between processes in continuous ti- me were considered since continuous time models dependence represent the first step in various applications, such as in finance, econometric practice, neuroscience, epidemiology, climatology, demographic, etc. In this dissertation the concept of dependence between stochastic processes and filtration is introduced. This concept is named causal predictability since it is focused on prediction. Some major characteristics of the given concept are shown and connections with known concept of dependence are explained. Finally, the concept of causal predictability is applied to the processes of di↵usion type, more precisely, to the uniqueness of weak solutions of Ito stochastic di↵erential equations and stochastic di↵erential equations with driving semi- martingales. Also, the representation theorem in terms of causal predictability is established and numerous examples of applications of the given concept are presented such as application in financial mathematics in the view of modeling default risk, in Bayesian statistics. The idea for the future might be to deal with the case of progressive stochastic predictability, i.e. the generalization of stochastic predictability from fixed time to stopping time. URI: http://hdl.handle.net/123456789/5572 Files in this item: 1
DOKTORAT_finalnaVerzija.pdf ( 1.785Mb ) -
Milošević, Petar (Beograd , 2015)[more][less]
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Konaković, Aleksandra (Beograd , 2014)[more][less]
URI: http://hdl.handle.net/123456789/3857 Files in this item: 1
MasterRadMinaAleksandraKonakovic.pdf ( 913.2Kb ) -
Jocković, Jelena (Beograd , 2012)[more][less]
Abstract: Statistical methodology for dealing with extremes depend on how extreme values are defined. One way to extract extremes from a given sample x1, x2, ..., xN is to consider maxima (minima). The other way is to consider values y1 = x1 − u, y2 = x2 − u, . . . , yn = xn − u, where y1, y2, . . . , yn are sample members above (below) a given predetermined threshold u. These two methods lead to two different approaches in extreme value theory. This doctoral dissertation has two main goals. One of them is to apply the techniques from extreme value framework to certain type of combinatorial problems. The other goal is to contribute to the field of statistical modeling of extremes. The dissertation consists of three chapters. In the first chapter, we introduce generalized extreme value distributions and generalized Pareto distributions (GPD). These two families play key roles in the two approaches to modeling extremes. We set out the theoretical background for both approaches. In the second chapter, we apply the extremal techniques to combinatorial waiting time problems. Precisely, we consider Coupon collector’s problem, defined as follows: elements are sampled with replacement from the set Nn = {1, 2, . . . , n} under assumption that each element has probability 1/n of being drawn. The subject of interest is the waiting time Mn until all elements of Nn or some other pattern are sampled. We focus our attention to the following two cases: 1. Mn is the waiting time until all elements of Nn are sampled at least r times, where r is a positive integer; 2. Mn is the waiting time until all pairs of elements jj, j ∈ Nn are sampled. We present new results related to the asymptotic behavior of the waiting time Mn, if it is known that a large number of trials was performed and the experiment is not over. For both cases, we determine the limiting distribution of exceedances of Mn over high thresholds, and answer some related questions: how to choose a suitable high threshold (depending on n) in order to obtain a limiting distribution; under what conditions the limit does not depend on the threshold; are the generalized Pareto distributions the only possible limits. We also estimate the speed of convergence in both cases. The third chapter of the dissertation is devoted to estimation of parameters and quantiles of the generalized Pareto distributions. We restrict the attention to the two-parameter version of GPD, defined as: Wγ,σ(x) = 1 − e−x , x ≥ 0, γ = 0 1 − 1 + γ σx −1 , x ≥ 0, γ > 0 1 − 1 + γ σx −1 , x ∈ h 0,−σ γ i , γ < 0. Well known problem with this model is inconsistency with the sample data, which is that one or more sample observations exceed the estimated upper bound in case when γ < 0. We propose a new, general technique to overcome the inconsistency problem and improve performance of the existing GPD estimation methods. We apply the proposed technique to methodof- moments and method-of-probability-weighted-moments estimates, investigate its performance through computer simulation and provide some real data examples. Finally, we address the problem of estimating high GPD quantiles. We evaluate the robustness of some estimation methods through simulation study and present a case study from finance (value-at-risk estimation), with special emphasis to certain difficulties related to this field of application. URI: http://hdl.handle.net/123456789/4271 Files in this item: 1
phdJockovic_Jelena.pdf ( 1.687Mb ) -
Jocković, M. Jelena (Belgrade , 2012)[more][less]
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Petruševski, Ljiljana (Belgrade , 1986)[more][less]
URI: http://hdl.handle.net/123456789/51 Files in this item: 1
phdLjiljanaPetrusevski.pdf ( 1.651Mb ) -
Kurepa, Đuro (Zagreb , 1960)[more][less]
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Barlat, F.; Chung, K.; Richmond, O. (International Journal of Plasticity , 1993)[more][less]
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Chambon, Rene; Callerie, Denis; Tamagnini, Claudio (ELSEVIER , 2004)[more][less]
URI: http://hdl.handle.net/123456789/3190 Files in this item: 1
Strain_space_gradient_plasticity.pdf ( 491.8Kb ) -
Dimitrijević, Sergije (Beograd , 1952)[more][less]
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Kulezić, Dragan (Beograd , 1996)[more][less]
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Oertel, Gerhard (New York - Oxford , 1996)[more][less]
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Marsden, E. Jerrold (Barkeley , 1983)[more][less]
URI: http://hdl.handle.net/123456789/3009 Files in this item: 1
Marsden_Rieman_metric_in_Elasticity.pdf ( 933.5Kb ) -
Naghdi, P. M. (Pergamon , 1960)[more][less]
URI: http://hdl.handle.net/123456789/3184 Files in this item: 1
Naghdi(Plasticity-1960).pdf ( 3.499Mb ) -
Bugarski, Ivan (Niš Cultural Center , 2007)[more][less]
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Todorović, Nataša (Beograd , 2012)[more][less]
Abstract: Nehoroševljeva teorema (1977) igra veoma značajnu ulogu u razumevanju prirode hamiltonijanskih sistema. Pored činjenice da daje prikaz celokupne dinamike faznog prostora, ova teorema obezbeđuje i stabilnost kvazi-integrabilnih sistema u smislu da se vremena stabilnosti dejstava eksponencijalno produžavaju smanjenjem parametra poremećaja. Jedan od uslova teoreme je da integrabilna aproksimacija hamiltonijanske funkcije zadovoljava takozvani uslov strmosti. Pretpostavka izneta u dokazu teoreme je da pored parametra poremećaja, i strmost utiče na stabilizaciju sistema. Cilj doktorske teze bio je da se ova pretpostavka proveri numeričkim putem, kao i da se ilustruje uticaj strmosti na dinamiku sistema. Korišćen je model četvorodimenzione kvazi-integrabilne strme simplektičke mape, na kojoj se intenzitet strmosti lako podešava pomoću odgovarajućeg parametra. Oslanjajući se na tzv. Brzi indikator Ljapunova (Fast Lyapunov Indicator- FLI), numeričku metodu za detekciju haosa, prikazana je rezonantna struktura modela i merena je difuzija Arnoldovog tipa na odabranoj rezonanci. U prvom delu eksperimenta su za fiksirane vrednosti parametra strmosti merene promene difuzije u odnosu na smanjenje poremećaja, i potvrđeno je da se vrednost eksponenata fitovane funkcije povećava (sistem se stabilizuje) što je sistem strmiji. Otkriveno je i da eksponencijalna funkcija u strmoj nekonveksnoj oblasti u izvesnom smislu osciluje, što je rezultat koji još uvek nema svoju teorijsku interpretaciju. Takođe, primećeno je da se sa povećanjem strmosti, hiperboličke tačke zajedno sa svojom okolinom izmeštaju sa rezonantne krive. I konačno, u eksperimentu u kojem je meren direktan uticaj strmosti na brzinu difuzije, potvrđena je pretpostavka iz dokaza Nehoroševljeve teoreme, o postojanju kritične vrednosti parametra strmosti. Za vrednosti koje su manje od kritične, strmost nema uticaj na brzinu difuzije, dok je za vrednosti koje su veće od kritične, ovaj uticaj eksponencijalan. Štaviše, vrednost eksponenta ove funkcije u izvesnom smislu ima opšti karakter to jest ne zavisi od ostalih parametara sistema. Detaljnije analitičko objašnjenje ovog rezulata, svakako je jedan od budućih zadataka u interpretaciji Nehoroševljeve teoreme. URI: http://hdl.handle.net/123456789/3569 Files in this item: 1
Ntodorovic_Doktorat_1R.pdf ( 2.955Mb )