National Repository of Grey Literature 43 records found  previous11 - 20nextend  jump to record: Search took 0.01 seconds. 
Local polynomial regression
Cigán, Martin ; Bašta, Milan (advisor) ; Maciak, Matúš (referee)
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametric approach of data fitting. This particular method is based on repetition of fitting data using weighted least squares estimate of the parameters of the polynomial model. The aim of this thesis is therefore revision of some properties of the weighted least squares estimate used in linear regression model and introduction of the non-robust method of local polynomial regression. Some statistical properties of the local polynomial regression estimate are derived. Conditional bias and conditional variance of the local polynomial regression estimate are then approximated using Monte Carlo method and compared with theoretical results. Powered by TCPDF (www.tcpdf.org)
Normality and its testing
Hájek, Štěpán ; Bašta, Milan (advisor) ; Klebanov, Lev (referee)
This thesis is concerned with normality and its testing. We often encounter with this topic when using statistical tests and models. Among others, examples such as t tests, analysis of variance and linear regression might be given. In this thesis these tests and models are overviewed and the consequences of the violation of the normality assumption are briefly mentioned. The following section describes statistical tests of normality. For example Shapiro-Wilk test or Anderson-Darling test are explored. For each test of normality is given test statistic and conditions for rejection of the null hypothesis. The last section provides a simulation study. The first part of this study is devoted to exploring whether the empirical relative frequency of Type I error corresponds to the nominal significance level of the test. The second part of the simulation study explores the power of normality tests against various alternatives. The results are summarized and discussed. 1
Technical analysis based on trade volumes and their effectivity from the point of view of future price movements
Chval, David ; Bašta, Milan (advisor) ; Zichová, Jitka (referee)
This Bachelor Thesis studies methods of technical analysis based on trade volume. The first two chapters are theoretical. They describe financial markets, their functions, properties and methods used in analysis of financial instrument. In the next section is described efficient market hypothesis, forms of efficiency and tests of this hypothesis. The third chapter is analytical. The idea that extreme trading activity predict future increase, or decrease of stock prices is investigated. Here is described methodology, data aquisition, analysis results and comparison with other similar research.
Modelling and forecasting seasonal time series
Jantoš, Milan ; Bašta, Milan (advisor) ; Helman, Karel (referee)
In this Master Thesis there are summarized basic methods for modelling time series, such as linear regression with seasonal dummy variables, exponential smoothing and SARIMA processes. The thesis is aimed on modelling and forecasting seasonal time series using these methods. Goals of the Thesis are to introduce and compare these methods using a set of 2184 seasonal time series followed by evaluation their prediction abilities. The main benefit of this Master Thesis is understanding of different aspects of forecasting time series and empirical verification of advantages and disadvantages these methods in field of creating predictions.
Gradient Boosting Machine and Artificial Neural Networks in R and H2O
Sabo, Juraj ; Bašta, Milan (advisor) ; Plašil, Miroslav (referee)
Artificial neural networks are fascinating machine learning algorithms. They used to be considered unreliable and computationally very expensive. Now it is known that modern neural networks can be quite useful, but their computational expensiveness unfortunately remains. Statistical boosting is considered to be one of the most important machine learning ideas. It is based on an ensemble of weak models that together create a powerful learning system. The goal of this thesis is the comparison of these machine learning models on three use cases. The first use case deals with modeling the probability of burglary in the city of Chicago. The second use case is the typical example of customer churn prediction in telecommunication industry and the last use case is related to the problematic of the computer vision. The second goal of this thesis is to introduce an open-source machine learning platform called H2O. It includes, among other things, an interface for R and it is designed to run in standalone mode or on Hadoop. The thesis also includes the introduction into an open-source software library Apache Hadoop that allows for distributed processing of big data. Concretely into its open-source distribution Hortonworks Data Platform.
Data-Snooping Biases in Backtesting
Krpálek, Jan ; Bašta, Milan (advisor) ; Malá, Ivana (referee)
In this paper, we utilize White's Reality Check, White (2000), and Hansen's SPA test, Hansen (2004), to evaluate technical trading rules while quantifying the data-snooping bias. Secondly, we discuss the result with Probability of Backtest Overfitting framework, introduced by Bailey et al. (2015). Hence, the study presents a comprehensive test of momentum trading across the US futures markets from 2004 to 2016. The evidence indicates that technical trading rules have not been pro?table in the US futures markets after correcting for the data snooping bias.
Building credit scoring models using selected statistical methods in R
Jánoš, Andrej ; Bašta, Milan (advisor) ; Pecáková, Iva (referee)
Credit scoring is important and rapidly developing discipline. The aim of this thesis is to describe basic methods used for building and interpretation of the credit scoring models with an example of application of these methods for designing such models using statistical software R. This thesis is organized into five chapters. In chapter one, the term of credit scoring is explained with main examples of its application and motivation for studying this topic. In the next chapters, three in financial practice most often used methods for building credit scoring models are introduced. In chapter two, the most developed one, logistic regression is discussed. The main emphasis is put on the logistic regression model, which is characterized from a mathematical point of view and also various ways to assess the quality of the model are presented. The other two methods presented in this thesis are decision trees and Random forests, these methods are covered by chapters three and four. An important part of this thesis is a detailed application of the described models to a specific data set Default using the R program. The final fifth chapter is a practical demonstration of building credit scoring models, their diagnostics and subsequent evaluation of their applicability in practice using R. The appendices include used R code and also functions developed for testing of the final model and code used through the thesis. The key aspect of the work is to provide enough theoretical knowledge and practical skills for a reader to fully understand the mentioned models and to be able to apply them in practice.
Ventilation and warm air heating of service station
Bašta, Milan ; Pavelek, Milan (referee) ; Janotková, Eva (advisor)
This diploma thesis is focused on the design project of ventilation and warm air heating of a service station. The thesis starts with the introduction into this issue and the layout of the service station is presented here. Afterwards, calculations of the heat penetration coefficient, heat losses and heat load, as well as the amount of fresh air are presented here. Furthermore, the amount of supply air is assigned in this part. Consequently, a design project of ventilation and air heating is set up based on these calculations. The design project also includes the choice, dimensioning of local exhausting, a slot diffuser and of air duct paths. Moreover, pressure losses are calculated and the choice of an ventilation and heating unit is made. A design documentation with material specifications is also part of this diploma thesis.
The Analysis of Selected Monthly Temperature Time Series in Europe
Janoutová, Eva ; Helman, Karel (advisor) ; Bašta, Milan (referee)
This Bachelor´s thesis is focused on analysis of time series of monthly mean temperatures and monthly maximum temperatures. These temperatures are observed on eight meteorological stations between the years 1949-2013. The data were obtained from the database of European Climate Assessment & Data. Stations are divided into two groups according to Köppen climate classification. The purpose of this bachelor thesis is to observe and compare the development of individual time series in both groups using basic characteristics and methods based on time series decomposition and to verify that the observed station´s climate type classification is correct. Another aim is to verify the hypothesis that monthly mean temperatures and monthly maximum temperatures has increased in the observed period.
GARCH models and R
Jánoš, Andrej ; Bašta, Milan (advisor) ; Hejdová, Martina (referee)
The work is devoted to the concept of volatility and the basic models of volatility ARCH and GARCH. Firstly, volatility, properties of volatility, general structure of the models and historical volatility is described. Then the ARCH and GARCH volatility models are introduced and their properties, estimation methods and the possibility of implementation of these models in modeling and forecasting volatility are discussed. A substantial part of this work is a detailed application of the described models to some particular time series (both simulated and real) using the R program. We analyze the real data capturing the evolution of Prague stock index PX. The key aspect of the work is to provide enough theoretical knowledge and practical skills for a reader to fully understand the mentioned models and to be able to apply them in practise.

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