Počet nalezených dokumentů: 786
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From John Graunt to Adolphe Quetelet: on the Origins Of Demography
Kalina, Jan
2023 - anglický
John Graunt (1620-1674) and Adolphe Quetelet (1796-1874) were two important personalities, who contributed to the origins of demography. As they both developed statistical techniques for the analysis of demographic data, they are important also from the point of view of history of statistics. The contributions of both Graunt and Quetelet especially to the development of mortality tables and models are recalled in this paper. Already from the 17th century, the available mortality tables were exploited for computing life annuities. Also the contribution of selected personalities inspired by Graunt are recalled here, the work of Christian Huygens, Jacob Bernoulli, or Abraham de Moivre is discussed to document that the historical development of statistics and probability theory was connected with the development of demography. Klíčová slova: history of demography; history of statistics; probability theory; moral statistics; mortality tables Dokument je dostupný na externích webových stránkách.
From John Graunt to Adolphe Quetelet: on the Origins Of Demography

John Graunt (1620-1674) and Adolphe Quetelet (1796-1874) were two important personalities, who contributed to the origins of demography. As they both developed statistical techniques for the analysis ...

Kalina, Jan
Ústav informatiky, 2023

Epidemiologické modely s agenty
Neruda, Roman
2023 - český
Tento příspěvek je jemným úvodem do problematiky agentních modelů a jejich aplikací v epidemiologickém modelování. Představíme agentní modely jednak z hlediska informatiky, jednak jako nástroj modelování v jiných vědních disciplínách. V příkladové studii ukážeme model s agenty a sociální sítí jejich kontaktů, který slouží pro simulaci vývoje epidemie a vlivu protiepidemických opatření. Plné texty jsou dostupné na jednotlivých ústavech Akademie věd ČR.
Epidemiologické modely s agenty

Tento příspěvek je jemným úvodem do problematiky agentních modelů a jejich aplikací v epidemiologickém modelování. Představíme agentní modely jednak z hlediska informatiky, jednak jako nástroj ...

Neruda, Roman
Ústav informatiky, 2023

Different Boundary Conditions For LES Solver PALM 6.0 Used for ABL in Tunnel Experiment
Řezníček, Hynek; Geletič, Jan; Bureš, Martin; Krč, Pavel; Resler, Jaroslav; Vrbová, Kateřina; Trush, Arsenii; Michálek, Petr; Beneš, L.; Sühring, M.
2023 - anglický
We tried to reproduce results measured in the wind tunnel experiment with a CFD simulation provided by numerical model PALM. A realistic buildings layout from the Prague-Dejvice quarter has been chosen as a testing domain because solid validation campaign for PALM simulation of Atmospheric Boundary Layer (ABL) over this quarter was documented in the past. The question of input data needed for such simulation and capability of the model to capture correctly the inlet profile and its turbulence structure provided by the wind-tunnel is discussed in the study The PALM dynamical core contains a solver for the Navier-Stokes equations. By default, the model uses the Large Eddy Simulation (LES) approach in which the bulk of the turbulent motions is explicitly resolved. It is well validated tool for simulations of the complex air-flow within the real urban canopy and also within its reduced scale provided by wind tunnel experiments. However the computed flow field between the testing buildings did not correspond well to the measured wind velocity in some points. Different setting of the inlet boundary condition was tested but none of them gave completely developed turbulent flow generated by vortex generators and castellated barrier wall place at the entrance of the aerodynamic section of the wind tunnel. Klíčová slova: large eddy simulation; wind tunnel; atmospheric boundary layer; PALM model; turbulence Dokument je dostupný na externích webových stránkách.
Different Boundary Conditions For LES Solver PALM 6.0 Used for ABL in Tunnel Experiment

We tried to reproduce results measured in the wind tunnel experiment with a CFD simulation provided by numerical model PALM. A realistic buildings layout from the Prague-Dejvice quarter has been ...

Řezníček, Hynek; Geletič, Jan; Bureš, Martin; Krč, Pavel; Resler, Jaroslav; Vrbová, Kateřina; Trush, Arsenii; Michálek, Petr; Beneš, L.; Sühring, M.
Ústav informatiky, 2023

On the structure and values of betweenness centrality in dense betweenness-uniform graphs
Ghanbari, B.; Hartman, David; Jelínek, V.; Pokorná, Aneta; Šámal, R.; Valtr, P.
2023 - anglický
Betweenness centrality is a network centrality measure based on the amount of shortest paths passing through a given vertex. A graph is betweenness-uniform (BUG)if all vertices have an equal value of betweenness centrality. In this contribution, we focus on betweenness-uniform graphs with betweenness centrality below one. We disprove a conjecture about the existence of a BUG with betweenness value α for any rational numberαfrom the interval (3/4,∞) by showing that only very few betweenness centrality values below 6/7 are attained for at least one BUG. Furthermore, among graphs with diameter at least three, there are no betweenness-uniform graphs with a betweenness centrality smaller than one. In graphs of smaller diameter, the same can be shown under a uniformity condition on the components of the complement. Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
On the structure and values of betweenness centrality in dense betweenness-uniform graphs

Betweenness centrality is a network centrality measure based on the amount of shortest paths passing through a given vertex. A graph is betweenness-uniform (BUG)if all vertices have an equal value of ...

Ghanbari, B.; Hartman, David; Jelínek, V.; Pokorná, Aneta; Šámal, R.; Valtr, P.
Ústav informatiky, 2023

Beyond the Erdős–Sós conjecture
Davoodi, Akbar; Piguet, Diana; Řada, Hanka; Sanhueza-Matamala, N.
2023 - anglický
We prove an asymptotic version of a tree-containment conjecture of Klimošová, Piguet and Rozhoň [European J. Combin. 88 (2020), 103106] for graphs with quadratically many edges. The result implies that the asymptotic version of the Erdős-Sós conjecture in the setting of dense graphs is correct. Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
Beyond the Erdős–Sós conjecture

We prove an asymptotic version of a tree-containment conjecture of Klimošová, Piguet and Rozhoň [European J. Combin. 88 (2020), 103106] for graphs with quadratically many edges. The result implies ...

Davoodi, Akbar; Piguet, Diana; Řada, Hanka; Sanhueza-Matamala, N.
Ústav informatiky, 2023

Statistical Method Selection Matters: Vanilla Methods in Regression May Yield Misleading Results
Kalina, Jan
2023 - anglický
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 and 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 illustrations reveal that choosing suitable methods represent an important (and often difficult) part of the analysis of economic data. Klíčová slova: linear regression; assumptions; non-standard situations; robustness; diagnostics Dokument je dostupný na externích webových stránkách.
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 informatiky, 2023

Fractionally Isomorphic Graphs and Graphons
Hladký, Jan; Hng, Eng Keat
2023 - anglický
Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
Fractionally Isomorphic Graphs and Graphons

Hladký, Jan; Hng, Eng Keat
Ústav informatiky, 2023

Rooting algebraic vertices of convergent sequences
Hartman, David; Hons, T.; Nešetřil, J.
2023 - anglický
Structural convergence is a framework for convergence of graphs by Nešetřil and Ossona de Mendez that unifies the dense (left) graph convergence and Benjamini-Schramm convergence. They posed a problem asking whether for a given sequence of graphs (Gn) converging to a limit L and a vertex r of L it is possible to find a sequence of vertices (rn) such that L rooted at r is the limit of the graphs Gn rooted at rn. A counterexample was found by Christofides and Král’; but they showed that the statement holds for almost all vertices r of L. We offer another perspective to the original problem by considering the size of definable sets to which the root r belongs. We prove that if r is an algebraic vertex (i.e. belongs to a finite definable set); the sequence of roots (rn) always exists. Plné texty jsou dostupné v digitálním repozitáři Akademie Věd.
Rooting algebraic vertices of convergent sequences

Structural convergence is a framework for convergence of graphs by Nešetřil and Ossona de Mendez that unifies the dense (left) graph convergence and Benjamini-Schramm convergence. They posed a problem ...

Hartman, David; Hons, T.; Nešetřil, J.
Ústav informatiky, 2023

Some Robust Approaches to Reducing the Complexity of Economic Data
Kalina, Jan
2023 - anglický
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. Klíčová slova: dimensionality reduction; Big Data; variable selection; robustness; sparsity Dokument je dostupný na externích webových stránkách.
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 informatiky, 2023

The 2022 Election in the United States: Reliability of a Linear Regression Model
Kalina, Jan; Vidnerová, Petra; Večeř, M.
2023 - anglický
In this paper, the 2022 United States election to the House of Representatives is analyzed by means of a linear regression model. After the election process is explained, the popular vote is modeled as a response of 8 predictors (demographic characteristics) on the state-wide level. The main focus is paid to verifying the reliability of two obtained regression models, namely the full model with all predictors and the most relevant submodel found by hypothesis testing (with 4 relevant predictors). Individual topics related to assessing reliability that are used in this study include confidence intervals for predictions, multicollinearity, and also outlier detection. While the predictions in the submodel that includes only relevant predictors are very similar to those in the full model, it turns out that the submodel has better reliability properties compared to the full model, especially in terms of narrower confidence intervals for the values of the popular vote. Klíčová slova: elections results; electoral demography; linear regression; reliability; variability Dokument je dostupný na externích webových stránkách.
The 2022 Election in the United States: Reliability of a Linear Regression Model

In this paper, the 2022 United States election to the House of Representatives is analyzed by means of a linear regression model. After the election process is explained, the popular vote is modeled ...

Kalina, Jan; Vidnerová, Petra; Večeř, M.
Ústav informatiky, 2023

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