Number of found documents: 1571
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Bank Survival Around the World: A Meta-Analytic Review
Kočenda, Evžen; Iwasaki, I.
2021 - English
Bank survival is essential to economic growth and development because banks mediate the financing of the economy. A bank’s overall condition is often assessed by a supervisory rating system called CAMELS, an acronym for the components Capital adequacy, Asset quality, Management quality, Earnings, Liquidity, and Sensitivity to market risk. Estimates of the impact of CAMELS components on bank survival vary widely. We perform a meta-synthesis and meta-regression analysis (MRA) using 2120 estimates collected from 50 studies. In the MRA, we account for uncertainty in moderator selection by employing Bayesian model averaging. The results of the synthesis indicate an economically negligible impact of CAMELS variables on bank survival; in addition, the effect of bank-specific, (macro)economic, and market factors is virtually absent. The results of the heterogeneity analysis and publication bias analysis are consistent in terms that they do not find an economically significant impact of the CAMELS variables. Moreover, best practice estimates show a small economic impact of CAMELS components and no impact of other factors. The study concludes that caution should be exercised when using CAMELS rating to predict bank survival or failure. Keywords: bank survival; bank failure; CAMELS; meta-analysis; publication selection bias Fulltext is available at external website.
Bank Survival Around the World: A Meta-Analytic Review

Bank survival is essential to economic growth and development because banks mediate the financing of the economy. A bank’s overall condition is often assessed by a supervisory rating system called ...

Kočenda, Evžen; Iwasaki, I.
Ústav teorie informace a automatizace, 2021

Does the Spillover Index Respond Significantly to Systemic Shocks? A Bootstrap-Based Probabilistic Analysis
Greenwood-Nimmo, M.; Kočenda, Evžen; Nguyen, V. H.
2021 - English
The spillover index developed by Diebold and Yilmaz (Economic Journal, 2009, vol. 119, pp. 158-171) is widely used to measure connectedness in economic and financial networks. Abrupt increases in the spillover index are typically thought to result from systemic events, but evidence of the statistical significance of this relationship is largely absent from the literature. We develop a new bootstrap-based technique to evaluate the probability that the value of the spillover index changes over an arbitrary time period following an exogenously defined event. We apply our framework to the original dataset studied by Diebold and Yilmaz and obtain qualified support for the notion that the spillover index increases in a timely and statistically significant manner in the wake of systemic shocks. Keywords: Spillover index; systemic events; bootstrap-after-bootstrap procedure Fulltext is available at external website.
Does the Spillover Index Respond Significantly to Systemic Shocks? A Bootstrap-Based Probabilistic Analysis

The spillover index developed by Diebold and Yilmaz (Economic Journal, 2009, vol. 119, pp. 158-171) is widely used to measure connectedness in economic and financial networks. Abrupt increases in the ...

Greenwood-Nimmo, M.; Kočenda, Evžen; Nguyen, V. H.
Ústav teorie informace a automatizace, 2021

Distributed Sequential Zero-Inflated Poisson Regression
Žemlička, R.; Dedecius, Kamil
2021 - English
The zero-inflated Poisson regression model is a generalized linear model (GLM) for non-negative count variables with an excessive number of zeros. This letter proposes its low-cost distributed sequential inference from streaming data in networks with information diffusion. The model is viewed as a probabilistic mixture of a Poisson and a zero-located Dirac component, whose probabilities are estimated using a quasi-Bayesian procedure. The regression coefficients are inferred by means of a weighted Bayesian update. The network nodes share their posterior distributions using the diffusion protocol.\n Keywords: Poisson regression; zero inflation; GLM Fulltext is available at external website.
Distributed Sequential Zero-Inflated Poisson Regression

The zero-inflated Poisson regression model is a generalized linear model (GLM) for non-negative count variables with an excessive number of zeros. This letter proposes its low-cost distributed ...

Žemlička, R.; Dedecius, Kamil
Ústav teorie informace a automatizace, 2021

Media Treatment of Monetary Policy Surprises and Their Impact on Firms’ and Consumers’ Expectations
Pinter, J.; Kočenda, Evžen
2021 - English
We empirically investigate whether monetary policy announcements affect firms’ and consumers’ expectations by taking into account media treatments of monetary policy announcements. To identify exogenous changes in monetary policy stances, we use the standard financial monetary policy surprise measures in the euro area. We then analyze how a general newspaper and a financial newspaper (Le Monde and The Financial Times) report on announcements. We find that 87 % of monetary policy surprises are either not associated with the general newspaper reporting a change in the monetary policy stance to their readers or have a sign that is inconsistent with the media report of the announcement. When we use the raw monetary policy surprises variable as an independent variable in the link between monetary policy announcements and firms’/consumers’ expectations, we mostly do not find, in line with several previous studies, any statistically significant association. When we take only monetary policy surprises that are consistent with the general newspaper report, in almost all cases we find that monetary policy surprises on the immediate monetary policy stance do affect expectations. Surprises related to future policy inclination and information shocks usually do not appear to matter. The results appear to be in line with rational inattention theories and highlight the need for caution in the use of monetary policy surprise measures for macroeconomic investigations. Keywords: firm expectations; consumer expectations; monetary policy surprises; European Central Bank; information effect Fulltext is available at external website.
Media Treatment of Monetary Policy Surprises and Their Impact on Firms’ and Consumers’ Expectations

We empirically investigate whether monetary policy announcements affect firms’ and consumers’ expectations by taking into account media treatments of monetary policy announcements. To identify ...

Pinter, J.; Kočenda, Evžen
Ústav teorie informace a automatizace, 2021

Mathematics and Optimal control theory meet Pharmacy: Towards application of special techniques in modeling, control and optimization of biochemical networks
Papáček, Štěpán; Matonoha, Ctirad; Duintjer Tebbens, Jurjen
2021 - English
Similarly to other scienti c domains, the expenses related to in silico modeling in pharmacology need not be extensively apologized. Vis a vis both in vitro and in vivo experiments, physiologically-based pharmacokinetic (PBPK) and pharmacodynamic models represent an important tool for the assessment of drug safety before its approval, as well as a viable option in designing dosing regimens. In this contribution, some special techniques related to the mathematical modeling, control and optimization of biochemical networks are presented on a paradigmatic example of enzyme kinetics. Keywords: Dynamical system; Systems pharmacology; Biochemical network; Input-output regulation; Optimization Fulltext is available at external website.
Mathematics and Optimal control theory meet Pharmacy: Towards application of special techniques in modeling, control and optimization of biochemical networks

Similarly to other scienti c domains, the expenses related to in silico modeling in pharmacology need not be extensively apologized. Vis a vis both in vitro and in vivo experiments, ...

Papáček, Štěpán; Matonoha, Ctirad; Duintjer Tebbens, Jurjen
Ústav teorie informace a automatizace, 2021

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

Multimodal data fusion in remote sensing
Greško, Šimon; Zitová, Barbara
2021 - English
With fast development of remote sensing technologies, demand for remote sensing data started to be increasing. Providing space resolution of these data may not be sufficient for target application. For that reason the methods for fusing remote sensing images were developed. This issue appears in the question of demand for high resolution thermal data. Sentinel-3 provides free remote sensing thermal data with space resolution 1000x1000 m, which may be insufficient for some specialized applications. In this thesis, standard methods of image fusion are presented as well as specialized methods for thermal sharpening, their modifications, implementation and results including validation and comparison. Keywords: remote sensing; thermal sharpening; sentinel Fulltext is available at external website.
Multimodal data fusion in remote sensing

With fast development of remote sensing technologies, demand for remote sensing data started to be increasing. Providing space resolution of these data may not be sufficient for target application. ...

Greško, Šimon; Zitová, Barbara
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

Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India
Nugent, Sh.; Quinn, Anthony
2021 - English
Existing frameworks for multi-task learning [1],[2] often rely on completely modelled relationships between tasks, which may not be available. Recent work [3], [4] has been undertaken on approaches to fully probabilistic methods for transfer learning between two Gaussian Process (GP) tasks. There, the target algorithm accepts source knowledge in the form of a probabilistic prior from a source algorithm, without requiring the target to model their interaction with the source. These strategies have offered robust improvements on current state of the art algorithms, such as the Intrinsic Coregionalization Model. The Bayesian Transfer Learning algorithm proposed in [4], was found to provide robust, positive\ntransfer. This algorithm was then extended to accommodate knowledge transfer from multiple source modellers [5]. Improved predictive performance was observed from increases in the number of sources. This report reviews the multi-source transfer findings in [5] and applies it to a real world problem of pollution modelling in India, using public-domain data. Keywords: fully probabilistic methods; Bayesian Transfer Learning algorithm; Gaussian Process; Intrinsic Coregionalization Model; pollution modelling Fulltext is available at external website.
Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India

Existing frameworks for multi-task learning [1],[2] often rely on completely modelled relationships between tasks, which may not be available. Recent work [3], [4] has been undertaken on approaches to ...

Nugent, Sh.; Quinn, Anthony
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

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