Number of found documents: 1572
Published from to

TESTING THE METHOD OF MULTIPLE SCALES AND THE AVERAGING PRINCIPLE FOR MODEL PARAMETER ESTIMATION OF QUASIPERIODIC TWO TIME-SCALE MODELS
Papáček, Štěpán; Matonoha, Ctirad
2023 - English
Some dynamical systems are characterized by more than one timescale, e.g. two well separated time-scales are typical for quasiperiodic systems. The aim of this paper is to show how singular perturbation methods based on the slow-fast decomposition can serve for an enhanced parameter estimation when the slowly changing features are rigorously treated. Although the ultimate goal is to reduce the standard error for the estimated parameters, here we test two methods for numerical approximations of the solution of associated forward problem: (i) the multiple time-scales method, and (ii) the method of averaging. On a case study, being an under-damped harmonic oscillator containing two state variables and two parameters, the method of averaging gives well (theoretically predicted) results, while the use of multiple time-scales method is not suitable for our purposes. Keywords: Dynamical system; Singular perturbation; Averaging; Parameter estimation; Slow-fast decomposition; Damped oscillations Fulltext is available at external website.
TESTING THE METHOD OF MULTIPLE SCALES AND THE AVERAGING PRINCIPLE FOR MODEL PARAMETER ESTIMATION OF QUASIPERIODIC TWO TIME-SCALE MODELS

Some dynamical systems are characterized by more than one timescale, e.g. two well separated time-scales are typical for quasiperiodic systems. The aim of this paper is to show how singular ...

Papáček, Štěpán; Matonoha, Ctirad
Ústav teorie informace a automatizace, 2023

GA 19-07635S: Outputs and Results
Rehák, Branislav
2023 - English
This manuscript aims to deliver a survey of results obtained during the solution of the project No. GA19-07635S of the Czech Science Foundation. The timespan dedicated to the work on this project was 1.3.2019 - 30.6.2022. The main area dealt with were\nnonlinear multi-agent systems and their synchronization, further, attention was paid to some auxiliary results in the area of nonlinear observers. This Report briefly introduces the Project, provides a summary of the results obtained and also sketches an outline how these results will be applied and extended in future. Keywords: multi-agent systems; nonlinear multi-agent systems; synchronization Fulltext is available at external website.
GA 19-07635S: Outputs and Results

This manuscript aims to deliver a survey of results obtained during the solution of the project No. GA19-07635S of the Czech Science Foundation. The timespan dedicated to the work on this project was ...

Rehák, Branislav
Ústav teorie informace a automatizace, 2023

Ambiguity in Stochastic Optimization Problems with Nonlinear Dependence on a Probability Measure via Wasserstein Metric
Kaňková, Vlasta
2023 - English
Many economic and financial applications lead to deterministic optimization problems depending on a probability measure. It happens very often (in applications) that these problems have to be solved on the data base. Point estimates of an optimal value and estimates of an optimal solutionset can be obtained by this approach. A consistency, a rate of convergence and normal properties, of these estimates, have been discussed (many times) not only under assumptions of independent data corresponding to the distributions with light tails, but also for weak dependent data and the distributions with heavy tails. However, it is also possible to estimate (on the data base) a confidence intervals and bounds for the optimal value and the optimal solutions. To analyze this approach we focus on a special case of static problems depending nonlineary on the probability measure. Stability results based on the Wasserstein metric and the Valander approach will be employed for the above mentioned analysis. Keywords: Stochastic optimization problems; static problems; empirical measure; point estimates; interval estimates; nonlinear dependence Fulltext is available at external website.
Ambiguity in Stochastic Optimization Problems with Nonlinear Dependence on a Probability Measure via Wasserstein Metric

Many economic and financial applications lead to deterministic optimization problems depending on a probability measure. It happens very often (in applications) that these problems have to be solved ...

Kaňková, Vlasta
Ústav teorie informace a automatizace, 2023

COMPUTER SIMULATION STUDY OF THE STABILITY OF UNDERACTUATED BIPEDAL ROBOT MODELS (motivated by Griffin and Grizzle, 2017)
Polach, P.; Anderle, Milan; Zezula, Pavel; Papáček, Štěpán
2023 - English
A key feature for bipedal walkers (robots and humans as well) is their stability or disturbance rejection defined as the ability to deal with unexpected disturbances. The paper by Griffin and Grizzle (2017) have significantly contributed to the shift from flat ground to slopes and steps when evaluating the walking efficiency of their robots. Similarly, in this contribution, based on the appropriate model of robot dynamics and control law, we examine the stability of walking-without-falling for different ground perturbations for a threelink compass gait walker. I.e., we perform the sensitivity analysis of the walking stability of underactuated bipedal walker with respect to certain disturbation using the alaska/MultibodyDynamics simulation tool. Keywords: Mechatronics; Bipedal robot; Multibody dynamics; Acrobot; Control applications Fulltext is available at external website.
COMPUTER SIMULATION STUDY OF THE STABILITY OF UNDERACTUATED BIPEDAL ROBOT MODELS (motivated by Griffin and Grizzle, 2017)

A key feature for bipedal walkers (robots and humans as well) is their stability or disturbance rejection defined as the ability to deal with unexpected disturbances. The paper by Griffin and Grizzle ...

Polach, P.; Anderle, Milan; Zezula, Pavel; Papáček, Štěpán
Ústav teorie informace a automatizace, 2023

Texture Spectral Similarity Criteria Comparison
Havlíček, Michal; Haindl, Michal
2023 - English
Criteria capable of texture spectral similarity evaluation are presented and compared. From the fifteen evaluated criteria, only four criteria guarantee zero or minimal spectral ranking errors. Such criteria can support texture modeling algorithms by comparing the modeled texture with corresponding synthetic simulations. Another possible application is the development of texture retrieval, classification, or texture acquisition system. These criteria thoroughly test monotonicity and mutual correlation on specifically designed extensive monotonously degrading experiments. Keywords: Texture Comparison; Texture Modeling; Texture Retrieval; Texture Classification; Texture Acquisition Fulltext is available at external website.
Texture Spectral Similarity Criteria Comparison

Criteria capable of texture spectral similarity evaluation are presented and compared. From the fifteen evaluated criteria, only four criteria guarantee zero or minimal spectral ranking errors. Such ...

Havlíček, Michal; Haindl, Michal
Ústav teorie informace a automatizace, 2023

Statistical Method Selection Matters: Vanilla Methods in Regression May Yield Misleading Results
Kalina, Jan
2023 - English
The primary aim of this work is to illustrate the importance of the choice of the appropriate methods for the statistical analysis of economic data. Typically, there exist several alternative versions of common statistical methods for every statistical modeling task\nand the most habitually used (“vanilla”) versions may yield rather misleading results in nonstandard situations. Linear regression is considered here as the most fundamental econometric model. First, the analysis of a world tourism dataset is presented, where the number of international arrivals is modeled for 140 countries of the world as a response of 14 pillars (indicators) of the Travel and Tourism Competitiveness Index. Heteroscedasticity is clearly recognized in the dataset. However, the Aitken estimator, which would be the standard remedy in such a situation, is revealed here to be very inappropriate. Regression quantiles represent a much more suitable solution here. The second illustration with artificial data reveals standard regression quantiles to be unsuitable for data contaminated by outlying values. Their recently proposed robust version turns out to be much more appropriate. Both\nillustrations reveal that choosing suitable methods represent an important (and often difficult) part of the analysis of economic data. Keywords: linear regression; assumptions; non-standard situations; robustness; diagnostics Fulltext is available at external website.
Statistical Method Selection Matters: Vanilla Methods in Regression May Yield Misleading Results

The primary aim of this work is to illustrate the importance of the choice of the appropriate methods for the statistical analysis of economic data. Typically, there exist several alternative versions ...

Kalina, Jan
Ústav teorie informace a automatizace, 2023

Drivers of Private Equity Activity across Europe: An East-West Comparison
Kočenda, Evžen; Shivendra, R.
2023 - English
We investigate the key macroeconomic and institutional determinants of fundraising and investment activities and compare them across Europe, covering 13 Central and Eastern European (CEE) and 16 Western European (WE) countries. Five macroeconomic variables and nineteen institutional variables are selected. These variables are studied using panel data analysis with fixed effects and random effects models over an eleven-year observation period (2010–2020). Bayesian Model Averaging (BMA) is applied to select the key variables. Our results suggest that macroeconomic variables have no significant impact on fundraising and investment activity in either region. Investment activity is a significant driver of fundraising across Europe. Similarly, fundraising and divestment activity are significant drivers of investments across Europe. Institutional variables, however, affect fundraising and investment activity differently. While investment freedom has a significant effect on funds raised in the WE and CEE countries, government integrity and trade freedom are both significant determinants of investments in both European regions. In addition, the results demonstrate that, in contrast to the WE region, fundraising in the CEE region is not country specific. We investigate the key macroeconomic and institutional determinants of fundraising and investment activities and compare them across Europe, covering 13 Central and Eastern European (CEE) and 16 Western European (WE) countries. Five macroeconomic variables and nineteen institutional variables are selected. These variables are studied using panel data analysis with fixed effects and random effects models over an eleven-year observation period (2010–2020). Bayesian Model Averaging (BMA) is applied to select the key variables. Our results suggest that macroeconomic variables have no significant impact on fundraising and investment activity in either region. Investment activity is a significant driver of fundraising across Europe. Similarly, fundraising and divestment activity are significant drivers of investments across Europe. Institutional variables, however, affect fundraising and investment activity differently. While investment freedom has a significant effect on funds raised in the WE and CEE countries, government integrity and trade freedom are both significant determinants of investments in both European regions. In addition, the results demonstrate that, in contrast to the WE region, fundraising in the CEE region is not country specific. Keywords: Private equity; Fundraising; Investment Fulltext is available at external website.
Drivers of Private Equity Activity across Europe: An East-West Comparison

We investigate the key macroeconomic and institutional determinants of fundraising and investment activities and compare them across Europe, covering 13 Central and Eastern European (CEE) and 16 ...

Kočenda, Evžen; Shivendra, R.
Ústav teorie informace a automatizace, 2023

Bohl-Marek decomposition applied to a class of biochemical networks with conservation properties
Papáček, Štěpán; Matonoha, Ctirad; Duintjer Tebbens, Jurjen
2023 - English
This study presents an application of one special technique, further called as Bohl-Marek decomposition, related to the mathematical modeling of biochemical networks with mass conservation properties. We continue in direction of papers devoted to inverse problems of parameter estimation for mathematical models describing the drug-induced enzyme production networks [3]. However, being aware of the complexity of general physiologically based pharmacokinetic (PBPK) models, here we focus on the case of enzyme-catalyzed reactions with a substrate transport chain [5]. Although our ultimate goal is to develop a reliable method for fitting the model parameters to given experimental data, here we study certain numerical issues within the framework of optimal experimental design [6]. Before starting an experiment on a real biochemical network, we formulate an optimization problem aiming to maximize the information content of the corresponding experiment. For the above-sketched optimization problem, the computational costs related to the two formulations of the same biochemical network, being (i) the classical formulation x˙(t) = Ax(t) + b(t) and (ii) the 'quasi-linear' Bohl-Marek formulation x˙M(t) = M(x(t)) xM(t), can be determined and compared. Keywords: Mathematical modeling; Biochemical network; Pharmacokinetic (PBPK) models Fulltext is available at external website.
Bohl-Marek decomposition applied to a class of biochemical networks with conservation properties

This study presents an application of one special technique, further called as Bohl-Marek decomposition, related to the mathematical modeling of biochemical networks with mass conservation properties. ...

Papáček, Štěpán; Matonoha, Ctirad; Duintjer Tebbens, Jurjen
Ústav teorie informace a automatizace, 2023

Some Robust Approaches to Reducing the Complexity of Economic Data
Kalina, Jan
2023 - English
The recent advent of complex (and potentially big) data in economics requires modern and effective tools for their analysis including tools for reducing the dimensionality (complexity) of the given data. This paper starts with recalling the importance of Big Data in economics and with characterizing the main categories of dimension reduction techniques. While there have already been numerous techniques for dimensionality reduction available, this work is interested in methods that are robust to the presence of outlying measurements (outliers) in the economic data. Particularly, methods based on implicit weighting assigned to individual observations are developed in this paper. As the main contribution, this paper proposes three novel robust methods of dimension reduction. One method is a dimension reduction within a robust regularized linear regression, namely a sparse version of the least weighted squares estimator. The other two methods are robust versions of feature extraction methods popular in econometrics: robust principal component analysis and robust factor analysis. Keywords: dimensionality reduction; Big Data; variable selection; robustness; sparsity Fulltext is available at external website.
Some Robust Approaches to Reducing the Complexity of Economic Data

The recent advent of complex (and potentially big) data in economics requires modern and effective tools for their analysis including tools for reducing the dimensionality (complexity) of the given ...

Kalina, Jan
Ústav teorie informace a automatizace, 2023

Good vs. Bad Volatility in Major Cryptocurrencies: The Dichotomy and Drivers of Connectedness
Šíla, Jan; Kočenda, Evžen; Kukačka, Jiří; Krištoufek, Ladislav
2023 - English
Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. The results suggest that crypto connectedness reflects important events and exhibits more variable and cyclical dynamics than those of traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that information about market downturns spills over substantially faster than news about comparable market surges. Overall, the connectedness dynamics are predominantly driven by fundamental crypto factors, while the asymmetry measure also depends on macro factors such as the VIX index and the expected inflation. Keywords: Volatility; Dynamic connectedness; Asymmetric effects; Cryptocurrency Fulltext is available at external website.
Good vs. Bad Volatility in Major Cryptocurrencies: The Dichotomy and Drivers of Connectedness

Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. ...

Šíla, Jan; Kočenda, Evžen; Kukačka, Jiří; Krištoufek, Ladislav
Ústav teorie informace a automatizace, 2023

About project

NRGL provides central access to information on grey literature produced in the Czech Republic in the fields of science, research and education. You can find more information about grey literature and NRGL at service web

Send your suggestions and comments to nusl@techlib.cz

Provider

http://www.techlib.cz

Facebook

Other bases