Mgr. Petr Novák, Ph.D.

Mgr. Petr Novák, Ph.D.

Ing. Jitka Hrabáková, Ph.D.

Department of Applied Mathematics

The bachelor's thesis focuses on the implementation of regression parameter estimation in a semiparametric accelerated failure time model. The thesis first introduces survival analysis, the approach to survival analysis using counting processes, and then regression modeling in survival analysis. Subsequent chapters provide detailed descriptions of the chosen methods for estimating regression parameters of semiparametric accelerated failure time model and the constructed R package. The selected methods are tested at the end using both real and simulated data. An extensive simulation study was conducted on simulated data, where were selected methods tested with different scenarios including different procentual number of censoring, number of observations and different distributions of model deviations. The practical result of the thesis is an implemented R package that executes selected methods for estimating regression coefficients and includes all necessary tools for the semiparametric analysis of censored data.

Thesis on DSpace

Mgr. Petr Novák, Ph.D.

Ing. Jitka Hrabáková, Ph.D.

Department of Applied Mathematics

This thesis investigates and seeks to identify the optimal method for predicting victory in the team-based game League of Legends. The study employs Logistic Regression, Feedforward Neural Networks and Recurrent Neural Networks. The research involved obtaining and processing a large dataset of around 165,000 League of Legends matches. The models were compared based on accuracy, F1 score and expected calibration error (ECE). The logistic regression model emerged as the most effective, achieving an accuracy of 72.49% and an ECE of 0.0062 on the test data. The main outcome of this work is a mapping of the suitability of different machine learning approaches for the problem of predicting victory in League of Legends during the match.

Mgr. Petr Novák, Ph.D.

doc. Ing. Pavel Hrabák, Ph.D.

Department of Applied Mathematics

This bachelor's thesis focuses on modeling trends in the population development of world countries. The work explores and applies modern statistical methods for time series modeling with an emphasis on utilizing explanatory variables. The theoretical part introduces basic concepts of time series, statistical characteristics, stochastic processes, and models suitable for population prediction, such as linear regression, ARIMA models, and random forests. The practical part describes the collection and preprocessing of population data and relevant socioeconomic indicators from public sources. Subsequently, three different models with various approaches are trained and compared on this data, including addressing seasonality, change point detection, and outlier detection. Model performance is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. Based on these metrics, the ARIMAX model proved to be the most accurate, achieving the lowest MAPE values for both short-term (0.0092) and long-term predictions (0.0200), thus effectively combining the time structure of the data with the influence of exogenous variables.