National Repository of Grey Literature 158 records found  beginprevious88 - 97nextend  jump to record: Search took 0.01 seconds. 
Favoritism Under Social Pressure: Evidence From English Premier League
Herrmann, Vojtěch ; Večeř, Jan (advisor) ; Hlávka, Zdeněk (referee)
The aim of this thesis is to study the extent to which the English Premier League referees are influenced by social pressure, especially by the home support and by the general popularity of the teams. Using regression analysis, we compare the actual length of the overtime, which is fully in the competence of the referee, with the predicted one from the usual game stoppages. Then we try to identify factors that contribute to any possible discrepancy. Our results suggest that the games tend to be extended beyond the expectations when the outcome of the game still can change, i.e., when the score differential at the time 90:00 is either zero or one. However, this extra extension happens almost regardless of the playing teams and thus we find no evidence for referee bias towards any specific team. However, a small bias towards the group of "Big" teams has been found, but only in the games in which the score differential was different from one.
Confidence bands for regression curves
Zavřelová, Adéla ; Hlávka, Zdeněk (advisor) ; Maciak, Matúš (referee)
This thesis deals with the constructions of the confidence band for a linear regression model. Basic characteristics of a linear model are given and constructions of different confidence bands are described for models, where the relationship is set by a one variable function. The main focus is on bands of polynomial models.
Bayesian variable selection
Jančařík, Joel ; Komárek, Arnošt (advisor) ; Hlávka, Zdeněk (referee)
The selection of variables problem is ussual problem of statistical analysis. Solving this problem via Bayesian statistic become popular in 1990s. We re- view classical methods for bayesian variable selection methods and set a common framework for them. Indicator model selection methods and adaptive shrinkage methods for normal linear model are covered. Main benefit of this work is incorporating Bayesian theory and Markov Chain Monte Carlo theory (MCMC). All derivations needed for MCMC algorithms is provided. Afterward the methods are apllied on simulated and real data. 1
Quantile curves
Michl, Marek ; Hlávka, Zdeněk (advisor) ; Hlubinka, Daniel (referee)
Modeling of quantile curves is a common problem across various fields in today's practice. The topic of this thesis is estimating quantile curves in case of two-sample gradual change. That is, when a relationship between two continuous variables in two samples is of interest, where the relationship is the same for both samples until a certain value of the explanatory variable. From that point on the relationship can differ. The result of this thesis is a procedure for estimating quantile curves, which fulfill this concept. 1
Visual statistical inference
Jeliga, Jan ; Hlávka, Zdeněk (advisor) ; Maciak, Matúš (referee)
Graphs, and data visualization in general, play a important role in modern statistics. In this thesis, we address the possibility of using these for hypothesis testing. First, we introduce the concept of visual testing and define analogies for terms such as statistic or p-value and additionally we define the terms specific to visual testing. We demonstrate the method of visual testing on an example, where we parallelly perform a conventional test for the same data set and the same null and alternative hypothesis. Further, we inspect the possibility of use of Amazon Mechanical Turk for visual testing. We describe the design of visual test and present results of simulation experiments conducted in order to assess the power of the visual test and to compare it to conventional test. Powered by TCPDF (www.tcpdf.org)
Confidence Intervals for Quantiles
Horejšová, Markéta ; Kulich, Michal (advisor) ; Hlávka, Zdeněk (referee)
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are explained. Among nonparametric approaches, a special emphasis is dedicated to a recent method based on a multinomial distribution for calculating the overall confidence level of confidence intervals for all quantiles of interest using an efficient recursive algorithm, which is also described. Furthermore, a method based on Kolmogorov-Smirnov statistic or an asymptotic method using empirical distribution function and order statistics for quantile estimate are presented. A special parametric method for several quantiles of a normally distributed population is introduced along with a few of its modifications. Subsequently, a simulation is run to test the real coverage of the described theoretical methods. Powered by TCPDF (www.tcpdf.org)
Tests of independence for multivariate data
Kudlík, Michal ; Omelka, Marek (advisor) ; Hlávka, Zdeněk (referee)
Title: Tests of independence for multivariate data Author: Bc. Michal Kudlík Department: Department of Probability and Mathematical Statistics Supervisor: Ing. Marek Omelka, PhD., Department of Probability and Mathema- tical Statistics Abstract: This thesis is an overview of tests of independence for multidimensi- onal data. The report includes tests on independence of categorical and conti- nuous random variables, tests assuming normal distribution of data, asymptotic nonparametric tests and permutation tests with application of the Monte Carlo method. This thesis shows the suitability of tests with properly chosen real data and checks significance level and compares the strength of the selected tests by simulation study while using appropriate statistical software. Based on the simu- lation study the thesis discusses an appropriateness of the use of different tests for different situations. Keywords: independence, permutation and asymptotic tests of independence, Monte Carlo method, simulation study 1
Order statistics
Tělupil, Dominik ; Hlávka, Zdeněk (advisor) ; Jurečková, Jana (referee)
The aim of this work is to introduce general notions of theory of order statistics and the method of ranked set sampling (RSS). This statistical method is based on ranking of observed random variables which allows us to con- struct an unbiased estimator with lower variance than by using the method of sim- ple random sampling. In this work we will study estimators of the expected va- lue. Furthermore, we will investigate some properties of estimators based on RSS and some modifications of this method. The thesis contains a chapter about soft- ware simulations which verify the properties of estimators based on RSS. 1
Robustification of statistical and econometrical regression methods
Jurczyk, Tomáš ; Víšek, Jan Ámos (advisor) ; Hlávka, Zdeněk (referee) ; Malý, Marek (referee)
Title: Robustification of statistical and econometrical regression methods Author: Mgr. Tomáš Jurczyk Department: Department of probability and mathematical statistics Supervisor: prof. RNDr. Jan Ámos Víšek CSc., IES FSV UK Praha Abstract: Multicollinearity and outlier presence are two problems of data which can occur during the regression analysis. In this thesis we are interested mainly in situations where combined outlier-multicollinearity problem is present. We will show first the behavior of classical methods developed for overcoming one of these problems. We will investigate the functionality of methods proposed as robust multicollinearity detectors as well. We will prove that proposed two-step procedures (in one step typically based on robust regression methods) are failing in outlier detection and therefore also multicollinearity detection, if the strong multicollinearity is present in the majority of the data. We will propose a new one-step method as a candidate for the robust detector of multicollinearity as well as the robust ridge regression estimate. We will derive its properties, behavior and propose the diagnostic tools derived from that method. Keywords: multicollinearity, outliers, robust detector of multicollinearity, ro- bust ridge regression 1
Wavelet transform
Valter, Boris ; Hlávka, Zdeněk (advisor) ; Dvořák, Jiří (referee)
Wavelet transform is a term from signal analysis. It is mostly used in physics, but also in finance, where we can use it to find a trend in different financial data. In the first chapter we will describe two older methods of signal analysis: Fourier transform and short-time Fourier transform. In the second chapter we show, how wavelet transform works, derive frequently used algorithm for calculating discrete wavelet transform and at the end we show several practical examples. This thesis was considered to deepen the knowledge of time-frequency analysis of signals, for better understanding of the principle, how wavelet transform works, and for potential extending its use. Powered by TCPDF (www.tcpdf.org)

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