Number of found documents: 1574
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REGULATORY NETWORK OF DRUG-INDUCED ENZYME PRODUCTION: PARAMETER ESTIMATION BASED ON THE PERIODIC DOSING RESPONSE MEASUREMENT
Papáček, Štěpán; Lynnyk, Volodymyr; Rehák, Branislav
2021 - English
The common goal of systems pharmacology, i.e. systems biology applied to the eld of pharmacology, is to rely less on trial and error in designing an input-output systems, e.g. therapeutic schedules. In this paper we present, on the paradigmatic example of a regulatory network of drug-induced enzyme production, the further development of the study published by Duintjer Tebbens et al. (2019) in the Applications of Mathematics. Here, the key feature is that the nonlinear model in form of an ODE system is controlled (or periodically forced) by an input signal being a drug intake. Our aim is to test the model features under both periodic and nonrecurring dosing, and eventually to provide an innovative method for a parameter estimation based on the periodic dosing response measurement. Keywords: Dynamical system; Regulatory network; Input-output; Regulation; Parameter estimation; FFT Fulltext is available at external website.
REGULATORY NETWORK OF DRUG-INDUCED ENZYME PRODUCTION: PARAMETER ESTIMATION BASED ON THE PERIODIC DOSING RESPONSE MEASUREMENT

The common goal of systems pharmacology, i.e. systems biology applied to the eld of pharmacology, is to rely less on trial and error in designing an input-output systems, e.g. therapeutic schedules. ...

Papáček, Štěpán; Lynnyk, Volodymyr; Rehák, Branislav
Ústav teorie informace a automatizace, 2021

Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election
Kalina, Jan; Vidnerová, P.
2021 - English
Regression quantiles can be characterized as popular tools for a complex modeling of a continuous response variable conditioning on one or more given independent variables. Because they are however vulnerable to leverage points in the regression model, an alternative approach denoted as implicitly weighted regression quantiles have been proposed. The aim of current work is to apply them to the results of the second round of the 2018 presidential election in the Czech Republic. The election results are modeled as a response of 4 demographic or economic predictors over the 77 Czech counties. The analysis represents the first application of the implicitly weighted regression quantiles to data with more than one regressor. The results reveal the implicitly weighted regression quantiles to be indeed more robust with respect to leverage points compared to standard regression quantiles. If however the model does not contain leverage points, both versions of the regression quantiles yield very similar results. Thus, the election dataset serves here as an illustration of the usefulness of the implicitly weighted regression quantiles. Keywords: linear regression; quantile regression; robustness, outliers; elections results Fulltext is available at external website.
Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election

Regression quantiles can be characterized as popular tools for a complex modeling of a continuous response variable conditioning on one or more given independent variables. Because they are however ...

Kalina, Jan; Vidnerová, P.
Ústav teorie informace a automatizace, 2021

A NUMERICAL METHOD FOR THE SOLUTION OF THE NONLINEAR OBSERVER PROBLEM
Rehák, Branislav
2021 - English
The central part in the process of solving the observer problem for nonlinear systems is to nd a solution of a partial differential equation of first order. The original method proposed to solve this equation used expansions into Taylor polynomials, however, it suffers from rather restrictive assumptions while the approach proposed here allows to generalize these requirements. Its characteristic feature is that it is based on the application of the Finite Element\nMethod. An illustrating example is provided. Keywords: Finite element method; Observer; Partial differential equation Fulltext is available at external website.
A NUMERICAL METHOD FOR THE SOLUTION OF THE NONLINEAR OBSERVER PROBLEM

The central part in the process of solving the observer problem for nonlinear systems is to nd a solution of a partial differential equation of first order. The original method proposed to solve ...

Rehák, Branislav
Ústav teorie informace a automatizace, 2021

Unsupervised Verification of Fake News by Public Opinion
Grim, Jiří
2021 - English
In this paper we discuss a simple way to evaluate the messages in social networks automatically, without any special content analysis or external intervention. We presume, that a large number of social network participants is capable of a relatively reliable evaluation of materials presented in the network. Considering a simple binary evaluation scheme (like/dislike), we propose a transparent algorithm with the aim to increase the voting power of reliable network members by means of weights. The algorithm supports the votes which correlate with the more reliable weighted majority and, in turn, the modified weights improve the quality of the weighted majority voting. In this sense the weighting is controlled only by a general coincidence of voting members while the specific content of messages is unimportant. The iterative optimization procedure is unsupervised and does not require any external intervention with only one exception, as discussed in Sec. 5.2 .\n\nIn simulation experiments the algorithm nearly exactly identifies the reliable members by means of weights. Using the reinforced weights we can compute for a new message the weighted sum of votes as a quantitative measure of its positive or negative nature. In this way any fake news can be recognized as negative and indicated as controversial. The accuracy of the resulting weighted decision making was essentially higher than a simple majority voting and has been considerably robust with respect to possible external manipulations.\n\nThe main motivation of the proposed algorithm is its application in a large social network. The content of evaluated messages is unimportant, only the related decision making of participants is registered and compared with the weighted vote with the aim to identify the most reliable voters. A large number of participants and communicated messages should enable to design a reliable and robust weighted voting scheme. Ideally the resulting weighted vote should provide a generally acceptable emotional feedback for network participants and could be used to indicate positive or controversial news in a suitably chosen quantitative way. The optimization algorithm has to be simple, transparent and intuitive to make the weighted vote well acceptable as a general evaluation tool.\n Keywords: weighted voting; unsupervised optimization Fulltext is available at external website.
Unsupervised Verification of Fake News by Public Opinion

In this paper we discuss a simple way to evaluate the messages in social networks automatically, without any special content analysis or external intervention. We presume, that a large number of ...

Grim, Jiří
Ústav teorie informace a automatizace, 2021

On kernel-based nonlinear regression estimation
Kalina, Jan; Vidnerová, P.
2021 - English
This paper is devoted to two important kernel-based tools of nonlinear regression: the Nadaraya-Watson estimator, which can be characterized as a successful statistical method in various econometric applications, and regularization networks, which represent machine learning tools very rarely used in econometric modeling. This paper recalls both approaches and describes their common features as well as differences. For the Nadaraya-Watsonestimator, we explain its connection to the conditional expectation of the response variable. Our main contribution is numerical analysis of suitable data with an economic motivation and a comparison of the two nonlinear regression tools. Our computations reveal some tools for the Nadaraya-Watson in R software to be unreliable, others not prepared for a routine usage. On the other hand, the regression modeling by means of regularization networks is much simpler and also turns out to be more reliable in our examples. These also bring unique evidence revealing the need for a careful choice of the parameters of regularization networks Keywords: Nonlinear regression; machine learning; kernel smoothing; regularization; regularization networks Fulltext is available at external website.
On kernel-based nonlinear regression estimation

This paper is devoted to two important kernel-based tools of nonlinear regression: the Nadaraya-Watson estimator, which can be characterized as a successful statistical method in various econometric ...

Kalina, Jan; Vidnerová, P.
Ústav teorie informace a automatizace, 2021

Research Report Influence of Vehicle Assistant System on Track keeping
Nedoma, P.; Herda, Z.; Plíhal, Jiří
2021 - English
Presented results describe different methods for the evaluation of car stability in lateral direction. Due to the significant differences between the tests, uniform methodology for recognizing the drives with ESC and without it was not determined. Two different methods for the drive on the circle and VDA were proposed instead. For evaluation criteria of vehicle stability with respect to base measured quantities, was used model with weight functions. Keywords: Electronic stability control; Vehicle Assistant System; Vehicle stability Fulltext is available at external website.
Research Report Influence of Vehicle Assistant System on Track keeping

Presented results describe different methods for the evaluation of car stability in lateral direction. Due to the significant differences between the tests, uniform methodology for recognizing the ...

Nedoma, P.; Herda, Z.; Plíhal, Jiří
Ústav teorie informace a automatizace, 2021

Ockham's Razor from a Fully Probabilistic Design Perspective
Hoffmann, A.; Quinn, Anthony
2021 - English
This research report investigates an approach to the design of an Ockham prior penalising parametric complexity in the Hierarchical Fully Probabilistic Design (HFPD) [1] setting. We identify a term which penalises the introduction of an additional parameter in the Wold decomposition. We also derive the objective Ockham Parameter Prior (OPI) in this context, based on earlier work [2], and we show that the two are, in fact, closely related. This confers validity on the HFPD Ockham term. Keywords: Ockham’s Razor; Hierarchical Fully Probabilistic Design; Parametric Inference; Fully Probabilistic Design Fulltext is available at external website.
Ockham's Razor from a Fully Probabilistic Design Perspective

This research report investigates an approach to the design of an Ockham prior penalising parametric complexity in the Hierarchical Fully Probabilistic Design (HFPD) [1] setting. We identify a term ...

Hoffmann, A.; Quinn, Anthony
Ústav teorie informace a automatizace, 2021

Institutions, Financial Development, and Small Business Survival: Evidence from European Emerging Economies
Iwasaki, I.; Kočenda, Evžen; Shida, Y.
2020 - English
In this paper, we traced the survival status of 94,401 small businesses in 17 European emerging markets from 2007–2017 and empirically examined the determinants of their survival, focusing on institutional quality and financial development. We found that institutional quality and level of financial development exhibit statistically significant and economically meaningful impacts on the survival probability of the SMEs being researched. The evidence holds even when we control for a set of firm-level characteristics such as ownership structure, financial performance, firm size, and age. The findings are also uniform across industries and country groups and robust beyond the difference in assumption of hazard distribution. Keywords: small business; survival analysis; European emerging markets Fulltext is available at external website.
Institutions, Financial Development, and Small Business Survival: Evidence from European Emerging Economies

In this paper, we traced the survival status of 94,401 small businesses in 17 European emerging markets from 2007–2017 and empirically examined the determinants of their survival, focusing on ...

Iwasaki, I.; Kočenda, Evžen; Shida, Y.
Ústav teorie informace a automatizace, 2020

Financial Crime and Punishment: A Meta-Analysis
de Batz, L.; Kočenda, Evžen
2020 - English
We examine how the publication of intentional financial crimes committed by listed firms is interpreted by financial markets, using a systematic and quantitative review of existing empirical studies. Specifically, we conduct a meta-regression analysis and investigate the extent and nature of the impact that the publication of financial misconducts exerts on stock returns. We survey 111 studies, published between 1978 and 2020, with a total of 439 estimates from event studies. Our key finding is that the average abnormal returns calculated from this empirical literature are affected by a negative publication selection bias. Still, after controlling for this bias, our meta-analysis indicates that publications of financial crimes are followed by statistically significant negative abnormal returns, which suggests the existence of an informational effect. Finally, the MRA results demonstrate that crimes committed in common law countries, alleged crimes, and accounting crimes carry particularly weighty information for market participants. The results call for more transparency on side of enforcers along enforcement procedures, to foster timely and proportionate market reactions and support efficient markets. Keywords: Meta-Analysis; Event study; Financial Misconduct; Fraud; Financial Markets; Returns; Listed Companies; Information and Market Efficiency Fulltext is available at external website.
Financial Crime and Punishment: A Meta-Analysis

We examine how the publication of intentional financial crimes committed by listed firms is interpreted by financial markets, using a systematic and quantitative review of existing empirical studies. ...

de Batz, L.; Kočenda, Evžen
Ústav teorie informace a automatizace, 2020

Selective Attention in Exchange Rate Forecasting
Kapounek, S.; Kučerová, Z.; Kočenda, Evžen
2020 - English
We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market participants process only a limited amount of information. Our analysis includes more than 100,000 news articles relevant to the six most-traded foreign exchange currency pairs for the period of 1979–2016. We employ a dynamic model averaging approach to reduce model selection uncertainty and to identify time-varying probability to include regressors in our models. Our results show that smaller sizes models accounting for the presence of selective attention offer improved fitting and forecasting results. Specifically, we document a growing impact of foreign trade and monetary policy news on the euro/dollar exchange rate following the global financial crisis. Overall, our results point to the existence of selective attention in the case of most currency pairs. Keywords: exchange rate; selective attention; news; forecasting; dynamic model averaging Fulltext is available at external website.
Selective Attention in Exchange Rate Forecasting

We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market ...

Kapounek, S.; Kučerová, Z.; Kočenda, Evžen
Ústav teorie informace a automatizace, 2020

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