Highly Robust Estimation of the Autocorrelation Coefficient
Kalina, Jan; Vlčková, Katarína
2014 - anglický The classical autocorrelation coefficient estimator in the time series context is very sensitive to the presence of outlying measurements in the data. This paper proposes several new robust estimators of the autocorrelation coefficient. First, we consider an autoregressive process of the first order AR(1) to be observed. Robust estimators of the autocorrelation coefficient are proposed in a straightforward way based on robust regression. Further, we consider the task of robust estimation of the autocorrelation coefficient of residuals of linear regression. The task is connected to verifying the assumption of independence of residuals and robust estimators of the autocorrelation coefficient are defined based on the Durbin-Watson test statistic for robust regression. The main result is obtained for the implicitly weighted autocorrelation coefficient with small weights assigned to outlying measurements. This estimator is based on the least weighted squares regression and we exploit its asymptotic properties to derive an asymptotic test that the autocorrelation coefficient is equal to 0. Finally, we illustrate different estimators on real economic data, which reveal the advantage of the approach based on the least weighted squares regression. The estimator turns out to be resistant against the presence of outlying measurements. Klíčová slova: time series; autoregressive process; linear regression; robust econometrics Plné texty jsou dostupné na vyžádání prostřednictvím repozitáře Akademie věd. Highly Robust Estimation of the Autocorrelation Coefficient

The classical autocorrelation coefficient estimator in the time series context is very sensitive to the presence of outlying measurements in the data. This paper proposes several new robust estimators ...

Kalina, Jan; Vlčková, Katarína
Ústav informatiky, 2014

A Weather Risk Prediction System for Road Trip Planning
Krč, Pavel; Fuglík, Viktor; Juruš, Pavel; Kasanický, Ivan; Konár, Ondřej; Pelikán, Emil; Eben, Kryštof; Šucha, M.
2014 - anglický The paper presents first ideas of the MEDARD-RODOS project. The aim of the project is to develop a decision support system for road trip planning, reflecting the weather risks predicted from the NWP models implemented in the MEDARD system (www.medard-online.cz) and using the traffic information from the RODOS project (www.centrum-rodos.cz). Klíčová slova: weather; prediction; risk; system; road; planning; MEDARD; RODOS; NWP Plné texty jsou dostupné na vyžádání prostřednictvím repozitáře Akademie věd. A Weather Risk Prediction System for Road Trip Planning

The paper presents first ideas of the MEDARD-RODOS project. The aim of the project is to develop a decision support system for road trip planning, reflecting the weather risks predicted from the NWP ...

Krč, Pavel; Fuglík, Viktor; Juruš, Pavel; Kasanický, Ivan; Konár, Ondřej; Pelikán, Emil; Eben, Kryštof; Šucha, M.
Ústav informatiky, 2014

Robust Regularized Cluster Analysis for High-Dimensional Data
Kalina, Jan; Vlčková, Katarína
2014 - anglický This paper presents new approaches to the hierarchical agglomerative cluster analysis for high-dimensional data. First, we propose a regularized version of the hierarchical cluster analysis for categorical data with a large number of categories. It exploits a regularized version of various test statistics of homogeneity in contingency tables as the measure of distance between two clusters. Further, our aim is cluster analysis of continuous data with a large number of variables. Various regularization techniques tailor-made for high-dimensional data have been proposed, which have however turned out to suffer from a high sensitivity to the presence of outlying measurements in the data. As a robust solution, we recommend to combine two newly proposed methods, namely a regularized version of robust principal component analysis and a regularized Mahalanobis distance, which is based on an asymptotically optimal regularization of the covariance matrix. We bring arguments in favor of the newly proposed methods. Klíčová slova: cluster analysis; robust data mining; big data; regularization Plné texty jsou dostupné na jednotlivých ústavech Akademie věd ČR. Robust Regularized Cluster Analysis for High-Dimensional Data

This paper presents new approaches to the hierarchical agglomerative cluster analysis for high-dimensional data. First, we propose a regularized version of the hierarchical cluster analysis for ...

Kalina, Jan; Vlčková, Katarína
Ústav informatiky, 2014

On the Consistency of an Estimator for Hierarchical Archimedean Copulas
Górecki, J.; Hofert, M.; Holeňa, Martin
2014 - anglický The paper addresses an estimation procedure for hierarchical Archimedean copulas, which has been proposed in the literature. It is shown here that this estimation is not consistent in general. Furthermore, a correction is proposed, which leads to a consistent estimator. Klíčová slova: hierarchical Archimedean copula; Kendall distribution function; parameter estimation; structure determination; consistency Plné texty jsou dostupné na vyžádání prostřednictvím repozitáře Akademie věd. On the Consistency of an Estimator for Hierarchical Archimedean Copulas

The paper addresses an estimation procedure for hierarchical Archimedean copulas, which has been proposed in the literature. It is shown here that this estimation is not consistent in general. ...

Górecki, J.; Hofert, M.; Holeňa, Martin
Ústav informatiky, 2014

In-Hospital Death Prediction in Patients with Acute Coronary Syndrome
Monhart, Z.; Reissigová, Jindra; Zvárová, Jana; Grünfeldová, H.; Janský, P.; Vojáček, J.; Widimský, P.
2013 - anglický Klíčová slova: acute coronary syndrome; in-hospital death; prediction; multilevel logistic regression; non-PCI hospital Plné texty jsou dostupné na jednotlivých ústavech Akademie věd ČR. In-Hospital Death Prediction in Patients with Acute Coronary Syndrome

Monhart, Z.; Reissigová, Jindra; Zvárová, Jana; Grünfeldová, H.; Janský, P.; Vojáček, J.; Widimský, P.
Ústav informatiky, 2013

Objectification of a Choice of a Spa Treatment Plan for Arthritis of the Hip Joint
Och, F.; Medonos, J.; Hanzlíček, P.; Valenta, Zdeněk; Dvořák, V.; Zvárová, Jana
2013 - anglický Klíčová slova: decision-support; spa treatment; hip arthritis; statistical analysis Plné texty jsou dostupné na jednotlivých ústavech Akademie věd ČR. Objectification of a Choice of a Spa Treatment Plan for Arthritis of the Hip Joint

Och, F.; Medonos, J.; Hanzlíček, P.; Valenta, Zdeněk; Dvořák, V.; Zvárová, Jana
Ústav informatiky, 2013

Robustness Aspects of Knowledge Discovery
Kalina, Jan
2013 - anglický The sensitivity of common knowledge discovery methods to the presence of outlying measurements in the observed data is discussed as their major drawback. Our work is devoted to robust methods for information extraction from data. First, we discuss neural networks for function approximation and their sensitivity to the presence of noise and outlying measurements in the data. We propose to fit neural networks in a robust way by means of a robust nonlinear regression. Secondly, we consider information extraction from categorical data, which commonly suffers from measurement errors. To improve its robustness properties, we propose a regularized version of the common test statistics, which may find applications e.g. in pattern discovery from categorical data. Klíčová slova: machine learning; outliers; neural networks; robust estimation Plné texty jsou dostupné na jednotlivých ústavech Akademie věd ČR. Robustness Aspects of Knowledge Discovery

The sensitivity of common knowledge discovery methods to the presence of outlying measurements in the observed data is discussed as their major drawback. Our work is devoted to robust methods for ...

Kalina, Jan
Ústav informatiky, 2013

System for Selection of Relevant Information for Decision Support
Kalina, Jan; Seidl, L.; Zvára, K.; Grünfeldová, H.; Slovák, Dalibor; Zvárová, Jana
2013 - anglický Klíčová slova: decision support system; web-service; information extraction; high-dimension; gene expressions Plné texty jsou dostupné na jednotlivých ústavech Akademie věd ČR. System for Selection of Relevant Information for Decision Support

Kalina, Jan; Seidl, L.; Zvára, K.; Grünfeldová, H.; Slovák, Dalibor; Zvárová, Jana
Ústav informatiky, 2013

Source localization for EEG patterns relevant to motor imagery BCI control
Bobrov, P.; Frolov, A.; Húsek, Dušan; Tintěra, J.
2013 - anglický This work concerns spatial localization of sources of EEG patterns the most specific for control of the motor imagery based BCI. In our previous work we have shown that performance of Bayesian BCI classifier can be drastically improved by extraction of the most relevant independent components of the EEG signal. This paper presents the results of spatial localization of electrical brain activity sources which activity is reflected by the extracted components. The localization was performed by solving the inverse problem in EEG source localization, using individual finite-element head models. The sources were located in central sulcus (Brodmann area 3a), in the superior regions of post- and precentral gyri, and supplementary motor cortex. Klíčová slova: brain computer interface; inverse EEG problem; brain activity location; signal classification; independent component analysis Plné texty jsou dostupné na jednotlivých ústavech Akademie věd ČR. Source localization for EEG patterns relevant to motor imagery BCI control

This work concerns spatial localization of sources of EEG patterns the most specific for control of the motor imagery based BCI. In our previous work we have shown that performance of Bayesian BCI ...

Bobrov, P.; Frolov, A.; Húsek, Dušan; Tintěra, J.
Ústav informatiky, 2013

Statistical Expectation of High Energy Physics Data Sets Separation Algorithms
Hakl, František
2013 - anglický Article focuses on the application of the basic results of the statistical learning theory known as Probabilistic Approximately Correct learning in the evaluation and post-processing of unique physical data obtained from the detectors of particle accelerators. The aim of this article is not direct separation of the measured data but evaluation of the appropriateness of separation methods used. The main principles and results of the PAC learning theory are briefly summarized, the main characteristics of selected multivariable data separation algorithms are studied from the VC-dimension point of view. Finally, based on actual data sets obtained from Tevatron D$\emptyset$ experiment, some practical hints for separation method selection and numerical computation are derived. Klíčová slova: Probably Approximately Correct Learning; Refutability; HEP data separation; Neural networks; Decision trees; VC-dimension Plné texty jsou dostupné na jednotlivých ústavech Akademie věd ČR. Statistical Expectation of High Energy Physics Data Sets Separation Algorithms

Article focuses on the application of the basic results of the statistical learning theory known as Probabilistic Approximately Correct learning in the evaluation and post-processing of unique ...

Hakl, František
Ústav informatiky, 2013