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Vehicle Routing Problem
Kafka, Ondřej ; Branda, Martin (advisor) ; Hanzák, Tomáš (referee)
The thesis deals with optimization problems which arise at distribution planning. These problems can often be easily formulated as integer programming problems, but rarely can be solved using mixed integer programming techniques. Therefore, it is necessary to study the efficiency of heuristic algorithms. The main focus of the thesis is on the vehicle routing problem with time windows. A tabu search algorithm for this problem was developed and implemented. It uses integer programming to solve the set partitioning problem in order to find optimal distribution of all customers into feasible routes found during the search. The results of the classical integer programming approach, basic insertion heuristic and presented tabu search algorithm are compared in a numerical study.
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Capital Requirement for Operational Risk Modeling
Poláchová, Kateřina ; Orsáková, Martina (advisor) ; Hanzák, Tomáš (referee)
Operational risk is one of important concepts in financial institutions. It needs to be managed, measured and minimized. Bank has to hold capital requirements to cover potential losses from this risk. The aim of this work is to find, describe and apply a model determining how much capital is needed. This work is dedicated to Loss Distribution Approach based on modelling severity and frequency of losses separately for each business line and operational risk event type. With help of Monte Carlo method we can obtain total loss model by aggregating specific distribution functions. Resulting capital requirement is the sum of partial capital requirements of business line/event type that are 99,9% VaR of total loss. Keywords: Operational Risk, Loss Distribution Approach, Extreme Value Theory, Monte Carlo Simulation, Value-at-Risk
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Regression trees
Masaila, Aleh ; Hanzák, Tomáš (advisor) ; Zvára, Karel (referee)
Title: Regression trees Author: Aleh Masaila Department: Department of Probability and Mathematical Statistics Supervisor: Mgr.Tomáš Hanzák Abstract: Although regression and classification trees are used for data analysis for several decades, they are still in the shadow of more traditional methods such as linear or logistic regression. This paper aims to describe a couple of the most famous regression trees and introduce a new direction in this area - a combination of regression trees and committee methods, so called the regression forests. There is a practical part of work where we try properties, strengths and weaknesses of the examined methods on real data sets. Keywords: regression tree, CART, MARS, regression forest 1
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Methods of dynamical analysis of portfolio composition
Meňhartová, Ivana ; Hanzák, Tomáš (advisor) ; Cipra, Tomáš (referee)
Title: Methods of dynamical analysis of portfolio composition Author: Ivana Meňhartová Department: Department of Probability and Mathematical Statistics Supervisor: Mgr. Tomáš Hanzák, KPMS, MFF UK Abstract: In the presented thesis we study methods used for dynamic analysis of portfolio based on it's revenues. The thesis focuses on Kalman filter and local- ly weighted regression as two basic methods for dynamic analysis. It describes in detail theory for these methods as well as their utilization and it discusses their proper settings. Practical applications of both methods on artificial data and real data from Prague stock-exchange are presented. Using artificial data we demonstrate practical importance of Kalman filter's assumptions. Afterwards we introduce term multicolinearity as a possible complication to real data applicati- ons. At the end of the thesis we compare results and usage of both methods and we introduce possibility of enhancing Kalman filter by projection of estimations or by CUSUM tests (change detection tests). Keywords: Kalman filter, locally weighted regression, multicollinearity, CUSUM test
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Regression goodness-of-fit criteria according to dependent variable type
Šimsa, Filip ; Hanzák, Tomáš (advisor) ; Hlubinka, Daniel (referee)
This work is devoted to the description of linear, logistic, ordinal and multinominal regression models and interpretation of its parameters. Then it introduces a variety of quality indicators of mathematical models and the re- lations between them. It focuses mainly on the Gini coefficient and the coefficient of determination R2 . The first mentioned is established by modifying the Lorenz curve for ordinal and continuous variables and by comparing the estimated proba- bilities for nominal variable. The coefficient of determination R2 is newly defined for the nominal variable and is examined its relationship with Gini coefficient. As- suming normally distributed scores and errors of the model is numerically derived the relation between the Gini coefficient and the coefficient of determiantion for different distribution of continuous dependent variable. Theoretical calculations and definitions are illustrated on two real data sets. 1
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Gini coefficient maximization in binary logistic regression
Říha, Samuel ; Hanzák, Tomáš (advisor) ; Hlávka, Zdeněk (referee)
This Bachelor thesis describes a binary logistic regression model. By means of the term loss function a parameter estimation for the model is derived. A "rich" set of "proper" loss functions - beta family of Fisher-consistent loss functions - is defined. In the second part of the thesis, four basic goodness-of-fit criteria - Gini coefficient, C-statistics, Kolmogorov-Smirnov statistics and coefficient of determination R2 are defined. Further on, a possibility of parameter estimation by maximizing the Gini coefficient is analysed. Several algorithms are designed for this purpose. They are compared with so far existing methods in one simulated data set and three real ones. 1
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Estimation and goodness-of-fit criteria in logistic regression model
Ondrušková, Markéta ; Hanzák, Tomáš (advisor) ; Zvára, Karel (referee)
In this bachelor thesis we describe binary logistic regression model and estimation of model's parameters by maximum likelihood method. Then we propose algorithm for the least squares method. In the goodness-of-fit criteria part we define Lorenz curve, Gini coefficient, C-statistics, Kolmogorov-Smirnov statistics and coefficient of determination R2 . We derive their relation to different sample coefficients of correlation. We derive typical relation between Gini coeffi- cient, Kolmogorov-Smirnov statistics and newly also coefficient of determination R2 via model of normally distributed score of bad and good clients. These derived teoretical results are verified on three real data sets. Keywords: Binary logistic regression, maximum likelihood, ordinary least squa- res, Gini coefficient, coefficient of determination. 1
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Some problems of exponential smoothing
Čurda, David ; Hanzák, Tomáš (advisor) ; Komárek, Arnošt (referee)
In this work the several exponential smoothing type methods are briefly described, which are often used to smoothing and forecasting in the time series. Selected problems, that occur in described methods, are presented and in some cases there are the suggestions to their solution, which should tend to more suitable smoothing or to the better forecasts. It's shown how the methods are applied on different data and how the forecasts differ from each other. In conclusion the quality of modifications is evaluated.
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Exponential smoothing
Mikulka, Jakub ; Cipra, Tomáš (referee) ; Hanzák, Tomáš (advisor)
Nazev prace: Exponencialnivyrovnavani Autor: Jakub Mikulka Katedra: Katedra pravdepodobnosti a matematicke statistiky Vedouci bakalarske prace: Mgr. Tomas Hanzak e-mail vedouciho:hanzak@karlin.mff.cuni.cz Abstrakt: Prace se zabyva dvema metodami exponencialniho vyrovnavani pro nesezonni casove rady s lokalne linearnim trendem: Holtove metode a dvojitemu exponencialmmu vyrovnani (Brownove metode). Je ukazano, ze Brownova metoda je specialnim pnpadem Holtovy metody. Dale je uveden vztah procesu ARIMA(0, 2, 2) a Holtovy metody. Hlavni casti prace je teoreticke odvozeni hodnoty MSE a autokorelacniho koeficientu pfedpovedmch chyb Q pri pouziti Holtovy metody pro vsechny kombinace jejfch vyrovnavacich konstant za predpokladu generovani rady procesem ARIMA(0, 2, 2} pro vsechny hodnoty jeho parametru. Odvozene teoreticke vzorce jsou aplikovany tez na Brownovu metodu. Odvozene vzorce jsou pomoci simulaci overeny a vyzkouseny na realnych casovych radach. Jsou formulovany prakticke zavery tykajici se obou metod. Klicova slova: autokorelacni koeficient predpovednich chyb, Holtova metoda, dvojite exponencialni vyrovnavani,MSE, vyrovnavaci konstanty Abstract Title: Exponential smoothing Author: Jakub Mikulka Department: Department of Probability and Mathematical Statistics Supervisor: Mgr. Tomas Hanzak...
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Step by step credit risk model construction
Rychnovský, Michal ; Hanzák, Tomáš (referee) ; Charamza, Pavel (advisor)
Nazev pracc: Postnpna vyslavba modelu ohoduoconi kroditniho ri/,ika Autor: Michal Ryclmovsky Katedra: Kaledra pravdepoelobnejsti a maternal icke statistiky Vedouci bakalafske pracc: RNDr. Pavel Charam/a, CSc. E-mail vedouciho: pavol.charani/a''^media research.ex Abstrakt: Ciloni toto pracc je pfibli/it podstatu vvstavby skoringovych mo- eleln. Popisnjeme zde metodu logisticke regrese, odhaelovani jejich paramotrn a testovani jcjicli vy/,nanmosti. Na /aklado, proiiioiniych odds ratio potoin zavadimo indei>endence model jako odhad podminone saneo s]>laceni klienta.. Tento ... dale zoljecnHJinne pfidavanini vah jedmjtlivyni sku])inani a ka- tegoriini charakt.eristik klienta.. Ta.kto pficha/Jnie k WOE niodeln a jjlnemu logistickemn niodeln. Vennjeine se take nicfeni divcr/ilikacni schopnosti ino- deln pomoci Lorenxovy kfivky a Somerovy d statistiky jako odhadu Giuiho koeficientn. Nakonec a])likujeine popsane nietody na praktiekon vystavbn yk(')riiigovych niodeln a na realnych dateeh porovnanie vhodnost a di\erx,ifi- kacni scho])nost pi'edstavovanych niodelu. Soneast.i ])race je take vystup na. int.ernetovon encyklo]>edii \\ikiiiedia. Klicova slova: kreditni rixiko, skoringove niodely, logisticka. 1'egrese. Title: Step by step credit risk model construction Author: Michal Rychnovsky Department: Department...
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