Number of found documents: 1654
Published from to

A Measure of Variability WIthin Parametric Families of Continuous Distributions
Fabián, Zdeněk
2022 - English
A continuous probability measure on an open interval of the real line induces in it a unique geometry, "center of gravity" of which is the typical value of the distribution. In the paper is identified a score variance as a finite measure of variability of distributions with respect to the typical value and discussed its properties and methods of estimation. Itroducing a generalized Rao distance in the sample space one can appraise the precision of the estimate of the typical value. Keywords: scalar-valued score; score mean; score variance; distance in the sample space Available at various institutes of the ASCR
A Measure of Variability WIthin Parametric Families of Continuous Distributions

A continuous probability measure on an open interval of the real line induces in it a unique geometry, "center of gravity" of which is the typical value of the distribution. In the paper is identified ...

Fabián, Zdeněk
Ústav informatiky, 2022

Score correlation for skewed distributions
Fabián, Zdeněk
2022 - English
Based on the new concept of the scalar-valued score function of continuous distributions we introduce the score correlation coefficient ”tai-lored” to the assumed probabilistic model and study its properties by means of simulation experiments. It appeared that the new correlation method is useful for enormously skewed distributions. Keywords: Scalar-valued score; score coefficient of variation; Monte Carlo Available at various institutes of the ASCR
Score correlation for skewed distributions

Based on the new concept of the scalar-valued score function of continuous distributions we introduce the score correlation coefficient ”tai-lored” to the assumed probabilistic model and study its ...

Fabián, Zdeněk
Ústav informatiky, 2022

Introduction to statistical inference based on scalar-valued scores
Fabián, Zdeněk
2022 - English
In the report we maintain consistently the following point of view: Given a continuous model, there are not the observed values, which are to be used in probabilistic and statistical considerations, but their ”treated forms”,the values of the scalar-valued score function corresponding to the model. Based on this modified concept of the score function, we develop theory of score random variables, study their geometry and define their new characteristics, finite even in cases of heavy-tailed models. A generalization for parametric families provides a new approach to parametric point estimation. Keywords: continuous distributions; score mean; score variance; score moment estimation method; score distance Available at various institutes of the ASCR
Introduction to statistical inference based on scalar-valued scores

In the report we maintain consistently the following point of view: Given a continuous model, there are not the observed values, which are to be used in probabilistic and statistical considerations, ...

Fabián, Zdeněk
Ústav informatiky, 2022

A New Look to Information and Uncertainty of Continuous Distributions
Fabián, Zdeněk
2022 - English
We define information and uncertainty function of a family of continuous distributions. Their values are relative information and uncertainty of an observation from the given parametric family, their mean values are the generalized Fisher information and a new measure of variability, the score variance. In a series of examples we show why to use new concepts instead of the differential entropy. Keywords: Differential entropy; information function; uncertainty function; mean information of distribution Available at various institutes of the ASCR
A New Look to Information and Uncertainty of Continuous Distributions

We define information and uncertainty function of a family of continuous distributions. Their values are relative information and uncertainty of an observation from the given parametric family, their ...

Fabián, Zdeněk
Ústav informatiky, 2022

Large Perimeter Objects Surrounded by a 1.5D Terrain
Keikha, Vahideh
2022 - English
Given is a 1.5D terrain T , i.e., an x-monotone polygonal chain in R2. Our objective is to approximate the largest area or perimeter convex polygon with at most k vertices inside T . For a constant k > 0, we design an FPTAS that efficiently approximates such polygons within a factor (1 − ǫ). For the special case of the´largest-perimeter contained triangle in T , we design an O(n log n) time exact algorithm that matches the same result for the area measure. Available in digital repository of the ASCR
Large Perimeter Objects Surrounded by a 1.5D Terrain

Given is a 1.5D terrain T , i.e., an x-monotone polygonal chain in R2. Our objective is to approximate the largest area or perimeter convex polygon with at most k vertices inside T . For a constant k ...

Keikha, Vahideh
Ústav informatiky, 2022

Recent Trends in Machine Learning with a Focus on Applications in Finance
Kalina, Jan; Neoral, Aleš
2022 - English
Machine learning methods penetrate to applications in the analysis of financial data, particularly to supervised learning tasks including regression or classification. Other approaches, such as reinforcement learning or automated machine learning, are not so well known in the context of finance yet. In this paper, we discuss the advantages of an automated data analysis, which is beneficial especially if a larger number of datasets should be analyzed under a time pressure. Important types of learning include reinforcement learning, automated machine learning, or metalearning. This paper overviews their principles and recalls some of their inspiring applications. We include a discussion of the importance of the concept of information and of the search for the most relevant information in the field of mathematical finance. We come to the conclusion that a statistical interpretation of the results of theautomatic machine learning remains crucial for a proper understanding of the knowledge acquired by the analysis of the given (financial) data. Keywords: statistical learning; automated machine learning; metalearning; financial data analysis; stock market investing Fulltext is available at external website.
Recent Trends in Machine Learning with a Focus on Applications in Finance

Machine learning methods penetrate to applications in the analysis of financial data, particularly to supervised learning tasks including regression or classification. Other approaches, such as ...

Kalina, Jan; Neoral, Aleš
Ústav informatiky, 2022

The 2020 Election In The United States: Beta Regression Versus Regression Quantiles
Kalina, Jan
2021 - English
The results of the presidential election in the United States in 2020 desire a detailed statistical analysis by advanced statistical tools, as they were much different from the majority of available prognoses as well as from the presented opinion polls. We perform regression modeling for explaining the election results by means of three demographic predictors for individual 50 states: weekly attendance at religious services, percentage of Afroamerican population, and population density. We compare the performance of beta regression with linear regression, while beta regression performs only slightly better in terms of predicting the response. Because the United States population is very heterogeneous and the regression models are heteroscedastic, we focus on regression quantiles in the linear regression model. Particularly, we develop an original quintile regression map, such graphical visualization allows to perform an interesting interpretation of the effect of the demographic predictors on the election outcome on the level of individual states. Keywords: elections results; electoral demography; quantile regression; heteroscedasticity; outliers Fulltext is available at external website.
The 2020 Election In The United States: Beta Regression Versus Regression Quantiles

The results of the presidential election in the United States in 2020 desire a detailed statistical analysis by advanced statistical tools, as they were much different from the majority of available ...

Kalina, Jan
Ústav informatiky, 2021

DC 5.3 Základní statistický model velkého měřítka
Brabec, Marek; Malý, Marek; Malá, I.; Hladká, Adéla
2021 - Czech
BIBLIOGRAFICKÉ ÚDAJE: Výzkumná zpráva č. SS02030031-V94, evidenční č. ENV/2021/118018. Praha: ICS CAS, 2021. 47 s. ANOTACE: Obsahem tohoto dokumentu je popis prostorového statistického modelu velkého měřítka vyvinutého z dosavadních dat poskytnutých ČHMÚ. Prostorový model bude (po nezbytných aktualizacích a případných modifikacích daných jak časovým vývojem samotného znečištění, který lze očekávat např. v souvislosti s dopady pandemie covid-19, tak dalším vývojem statistické metodologie) v dalších letech používán jako podklad pro vývoj algoritmu prostorové optimalizace umístění měřicích stanic na základě statistického designu. Jde o několik variantních řešení, která zohledňují různé aspekty statistického chování pole koncentrací vybraných znečišťujících látek. This document describes suite of fundamental large-scale statistical models developed from data provided by CHMI (Czech Hydrometeorological Institute). The models were constructed in several variants, differing in complexity, detail and computational demands. Spatial models will be, after some further developments and modifications (necessary not only from the natural model evolution but also due to systematic changes brought e.g. by covid outbreak influences) used as the main input for optimization algorithms constructed for selection of measurement stations on the principles of statistical design theory and methods. Keywords: spatial field of pollutant concentration; geostatistics; GAM; INLA; spatially varying covariance model; Bayesian modeling Available in digital repository of the ASCR
DC 5.3 Základní statistický model velkého měřítka

BIBLIOGRAFICKÉ ÚDAJE: Výzkumná zpráva č. SS02030031-V94, evidenční č. ENV/2021/118018. Praha: ICS CAS, 2021. 47 s. ANOTACE: Obsahem tohoto dokumentu je popis prostorového statistického modelu velkého ...

Brabec, Marek; Malý, Marek; Malá, I.; Hladká, Adéla
Ústav informatiky, 2021

City simulation software for modeling, planning, and strategic assessment of territorial city units
Svítek, M.; Přibyl, O.; Vorel, J.; Garlík, B.; Resler, Jaroslav; Kozhevnikov, S.; Krč, Pavel; Geletič, Jan; Daniel, Milan; Dostál, R.; Janča, T.; Myška, V.; Aralkina, O.; Pereira, A. M.
2021 - English
SVÍTEK, M., PŘIBYL, O., VOREL, J., GARLÍK, B., RESLER, J., KOZHEVNIKOV, S., KRČ, P., GELETIČ, J., DANIEL, M., DOSTÁL, R., JANČA, T., MYŠKA, V., ARALKINA, O., PEREIRA, A. M. City simulation software for modeling, planning, and strategic assessment of territorial city units. 1.1. Prague: CTU & ICS CAS, 2021. Technical Report. ABSTRACT: The Smart Resilience City concept is a new vision of a city as a digital platform and eco-system of smart services where agents of people, things, documents, robots, and other entities can directly negotiate with each other on resource demand principals providing the best possible solution. It creates the smart environment making possible self-organization in sustainable or, when needed, resilient way of individuals, groups and the whole system objectives. Keywords: Smart city; City simulation; Energy resource-demand modelling; Environmental modelling; Synthetic population; Transport modelling Available on request at various institutes of the ASCR
City simulation software for modeling, planning, and strategic assessment of territorial city units

SVÍTEK, M., PŘIBYL, O., VOREL, J., GARLÍK, B., RESLER, J., KOZHEVNIKOV, S., KRČ, P., GELETIČ, J., DANIEL, M., DOSTÁL, R., JANČA, T., MYŠKA, V., ARALKINA, O., PEREIRA, A. M. City simulation software ...

Svítek, M.; Přibyl, O.; Vorel, J.; Garlík, B.; Resler, Jaroslav; Kozhevnikov, S.; Krč, Pavel; Geletič, Jan; Daniel, Milan; Dostál, R.; Janča, T.; Myška, V.; Aralkina, O.; Pereira, A. M.
Ústav informatiky, 2021

Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix
Turčičová, Marie; Mandel, J.; Eben, Kryštof
2021 - English
We present an ensemble filter that provides a rigorous covariance regularization when the underlying random field is Gaussian Markov. We use a linear model for the precision matrix (inverse of covariance) and estimate its parameters together with the analysis mean by the Score Matching method. This procedure provides an explicit expression for parameter estimators. The resulting analysis step formula is the same as in the traditional ensemble Kalman filter. Keywords: Score matching; ensemble filter; Gaussian Markov random field; covariance modelling Available at various institutes of the ASCR
Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix

We present an ensemble filter that provides a rigorous covariance regularization when the underlying random field is Gaussian Markov. We use a linear model for the precision matrix (inverse of ...

Turčičová, Marie; Mandel, J.; Eben, Kryštof
Ústav informatiky, 2021

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