National Repository of Grey Literature 65 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
How to down-weight observations in robust regression: A metalearning study
Kalina, Jan ; Pitra, Zbyněk
Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is becoming popular in statistical learning and there is an increasing number of metalearning applications also in the analysis of economic data sets. Still, not much attention has been paid to its limitations and disadvantages. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 30 data sets with economic background and perform a metalearning study over them as well as over the same data sets after an artificial contamination.
How to down-weight observations in robust regression: A metalearning study
Kalina, Jan ; Pitra, Zbyněk
Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is becoming popular in statistical learning and there is an increasing number of metalearning applications also in the analysis of economic data sets. Still, not much attention has been paid to its limitations and disadvantages. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 30 data sets with economic background and perform a metalearning study over them as well as over the same data sets after an artificial contamination. We focus on comparing the prediction performance of the least weighted squares estimator with various weighting schemes. A broader spectrum of classification methods is applied and a support vector machine turns out to yield the best results. While results of a leave-1-out cross validation are very different from results of autovalidation, we realize that metalearning is highly unstable and its results should be interpreted with care. We also focus on discussing all possible limitations of the metalearning methodology in general.
Statistical Methods for Regression Models With Missing Data
Nekvinda, Matěj ; Kulich, Michal (advisor) ; Omelka, Marek (referee)
The aim of this thesis is to describe and further develop estimation strategies for data obtained by stratified sampling. Estimation of the mean and linear regression model are discussed. The possible inclusion of auxiliary variables in the estimation is exam- ined. The auxiliary variables can be transformed rather than used in their original form. A transformation minimizing the asymptotic variance of the resulting estimator is pro- vided. The estimator using an approach from this thesis is compared to the doubly robust estimator and shown to be asymptotically equivalent.
ADVANCED REGRESSION MODELS
Rosecký, Martin ; Popela, Pavel (referee) ; Bednář, Josef (advisor)
This thesis summarizes latest findings about municipal solid waste (MSW) modelling. These are used to solve multivariable version of inverse prediction problem. It is not possible to solve such problem analytically, so heuristic framework using regression models and data reconciliation was developed. As a side product, models for MSW modelling using PCA (Principal Component Analysis) and LM (Linear Model) were created. These were compared with heuristic model called RF (Random Forest). Both of these models were also used for per capita MSW modelling. Theoretical parts about generalized linear models, data reconciliation and nonlinear programming are also included.
Computational tasks for Parallel data processing course
Horečný, Peter ; Rajnoha, Martin (referee) ; Mašek, Jan (advisor)
The goal of this thesis was to create laboratory excercises for subject „Parallel data processing“, which will introduce options and capabilities of Apache Spark technology to the students. The excercises focus on work with basic operations and data preprocessing, work with concepts and algorithms of machine learning. By following the instructions, the students will solve real world situations problems by using algorithms for linear regression, classification, clustering and frequent patterns. This will show them the real usage and advantages of Spark. As an input data, there will be databases of czech and slovak companies with a lot of information provided, which need to be prepared, filtered and sorted for next processing in the first excercise. The students will also get known with functional programming, because the are not whole programs in excercises, but just the pieces of instructions, which are not repeated in the following excercises. They will get a comprehensive overview about possibilities of Spark by getting over all the excercices.
Impact of Terrorism on Stock Markets
Koščo, Marek ; Červinka, Michal (advisor) ; Nevrla, Matěj (referee)
Terrorism generally induces negative mood in the society. Financial markets performance exhibits the contingency on the mood of their trading parti- cipants. The thesis enhances the understanding of this interrelated entities by analysing the situation from 2000 to 2015 at the 20 world largest mar- kets. Their composite indices are put under scrutiny employing a multifactor model, a difference equation and a logit model. The impact is confirmed and further discussed, while the logit model provides a simple framework for forecasting index returns just after an attack with more than 25 casualties. Keywords global financial markets, terrorism, multifactor model, difference equation, logit model
Estimating data with use of interval analysis
Pelikánová, Petra ; Horáček, Jaroslav (advisor) ; Černý, Michael (referee)
This work is focused on estimating interval data by real functions and interval functions. It presents possibilistic and necessity models of interval regression and compares its strong and week formulations. Further we describe algorithms of linear and nonlinear estimation. The application part is based on demonstration of tolerance method and subtracting tolerance method analysing real cases. 1
Interactive software tools for teaching signal and image processing
Had, Pavel ; Rajmic, Pavel (referee) ; Mangová, Marie (advisor)
This thesis deals with the development of interactive applets for educational purposes. There are four applets: linear image combinations, least squares method and linear regression, discrete linear convolution in 2D, and interpolation in 1D. Each part of this thesis consists of a theoretical analysis of a given problem and its implementation in JavaScript. Specific applets then illustrate the problem so that it can be easily understood.
Factors affecting mortgage market
Mrázek, Michal ; Radová, Jarmila (advisor) ; Cibulka, Jakub (referee)
This bachelor thesis focuses on current issues regarding mortgage loans. The thesis aims to characterize the impacts of new regulations and selected macroeconomic factors on the mortgage market. The first part defines mortgage loans, including relevant terms connected to the field. Then, the impact of new legislature on banks and consumers in the mortgage business is researched. Furthermore, the thesis focuses on reccomendations of Czech national bank concerning indicators of LTV, DTI and DSTI. The second part characterizes selected macroeconomic factors and their impact on the mortgage market. The selected macroeconomic factors are gross domestic product, inflation, unemployment and interest rates of mortgage loans. Firstly, the impact of these factors is researched on quantity and sum of new mortgage loans. Then, the macroeconomic indicators are quantified and analyzed using regression analysis.
Some of the macroeconomic effects of labour migration following the Czech Republic joining the European Union
Vystrčil, Tomáš ; Černý, Michal (advisor) ; Tomanová, Petra (referee)
When the Czech Republic joined the European Union in 2004, the number of foreign nationals working in the Czech Republic quickly grew, along with their impact on the national economy. The aim of this thesis is to analyse and compare the effects of foreign and native labour on the gross domestic product (GDP) of the Czech Republic. Then we shall compare the effects of immigrant labour originating from the EU with other foreign nationals. Using the ordinary least squares method to analyse time series data, it is shown that labour migration has a positive effect on the GDP growth and the results are statistically significant. It seems that labour from the EU is the most productive group, followed by the foreign labour from other countries.

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