Interval Matrices: Regularity Yields Singularity
Rohn, Jiří
2016 - anglický It is proved that regularity of an interval matrix implies singularity of two related interval matrices. Klíčová slova: interval matrix; regularity; singularity Plné texty jsou dostupné v digitálním repozitáři NUŠL Interval Matrices: Regularity Yields Singularity

It is proved that regularity of an interval matrix implies singularity of two related interval matrices.

Rohn, Jiří
Ústav informatiky, 2016

ALMA Development Plan Study: Solar Research with ALMA. Progress Report
Brajša, R.; Bárta, Miroslav; Skokić, Ivica; Bastian, T.S.; Shimojo, M.; White, S. M.; Iwai, K.
2016 - anglický A mid-term progress report of the research/development project Solar Research with ALMA: Development study. The project has been demanded by ESO to the Czech node of the European ALMA Regional Center. The main goal of the project is development of the specific Solar Observing Mode for ALMA observatory. The report on demand of ESO summarizes results of the WP2: Observing modes and ALMA software requirements. Technical ALMA requirements of solar observing mode are studied and summarised. The ALMA user software was reviewed and its changes complying with the Solar Observing Mode has been proposed. The report has been reviewed by international expert panel nominated by ESO.\n\n\n Klíčová slova: ALMA; solar research; observing modes Dokument je dostupný na externích webových stránkách. ALMA Development Plan Study: Solar Research with ALMA. Progress Report

A mid-term progress report of the research/development project Solar Research with ALMA: Development study. The project has been demanded by ESO to the Czech node of the European ALMA Regional Center. ...

Brajša, R.; Bárta, Miroslav; Skokić, Ivica; Bastian, T.S.; Shimojo, M.; White, S. M.; Iwai, K.
Astronomický ústav, 2016

Comparison of mixture-based classification with the data-dependent pointer model for various types of components
Likhonina, Raissa; Suzdaleva, Evgenia; Nagy, Ivan
2016 - anglický The presented report is devoted to the analysis of a data-dependent pointer model, whether it brings some advantages in comparison with a data-independent pointer model at simulation and estimation of components referring to different types of distribution, including categorical, uniform, exponential and state-space components for a dynamic data-dependent model, and normal components for a static data-dependent pointer model. Klíčová slova: mixture-based classification; data-dependent pointer; recurisive mixture estimation Dokument je dostupný na externích webových stránkách. Comparison of mixture-based classification with the data-dependent pointer model for various types of components

The presented report is devoted to the analysis of a data-dependent pointer model, whether it brings some advantages in comparison with a data-independent pointer model at simulation and estimation of ...

Likhonina, Raissa; Suzdaleva, Evgenia; Nagy, Ivan
Ústav teorie informace a automatizace, 2016

Linear ARX and state-space model with uniform noise: computation of first and second moments
Jirsa, Ladislav
2016 - anglický This report collects technical procedures used for computations of various estimates and keeps them in one place for internal purposes. The context concerns application of estimation of unknown parameters and states of linear model with uniformly distributed noise. Klíčová slova: uncertainty; bounded variable; uniform noise; linear model; model identification; state estimation Dokument je dostupný na externích webových stránkách. Linear ARX and state-space model with uniform noise: computation of first and second moments

This report collects technical procedures used for computations of various estimates and keeps them in one place for internal purposes. The context concerns application of estimation of unknown ...

Jirsa, Ladislav
Ústav teorie informace a automatizace, 2016

On Exact Heteroscedasticity Testing for Robust Regression
Kalina, Jan; Peštová, Barbora
2016 - anglický The paper is devoted to the least weighted squares estimator, which is one of highly robust estimators for the linear regression model. Novel permutation tests of heteroscedasticity are proposed. Also the asymptotic behavior of the permutation test statistics of the Goldfeld-Quandt and Breusch-Pagan tests is investigated. A numerical experiment on real economic data is presented, which also shows how to perform a robust prediction model under heteroscedasticity. Klíčová slova: robust estimation; outliers; variance; diagnostic tools; heteroscedasticity Plné texty jsou dostupné v digitálním repozitáři Akademie Věd. On Exact Heteroscedasticity Testing for Robust Regression

The paper is devoted to the least weighted squares estimator, which is one of highly robust estimators for the linear regression model. Novel permutation tests of heteroscedasticity are proposed. Also ...

Kalina, Jan; Peštová, Barbora
Ústav informatiky, 2016

Robust Regularized Discriminant Analysis Based on Implicit Weighting
Kalina, Jan; Hlinka, Jaroslav
2016 - anglický In bioinformatics, regularized linear discriminant analysis is commonly used as a tool for supervised classification problems tailormade for high-dimensional data with the number of variables exceeding the number of observations. However, its various available versions are too vulnerable to the presence of outlying measurements in the data. In this paper, we exploit principles of robust statistics to propose new versions of regularized linear discriminant analysis suitable for highdimensional data contaminated by (more or less) severe outliers. The work exploits a regularized version of the minimum weighted covariance determinant estimator, which is one of highly robust estimators of multivariate location and scatter. The performance of the novel classification methods is illustrated on real data sets with a detailed analysis of data from brain activity research. Klíčová slova: high-dimensional data; classification analysis; robustness; outliers; regularization Plné texty jsou dostupné v digitálním repozitáři NUŠL Robust Regularized Discriminant Analysis Based on Implicit Weighting

In bioinformatics, regularized linear discriminant analysis is commonly used as a tool for supervised classification problems tailormade for high-dimensional data with the number of variables ...

Kalina, Jan; Hlinka, Jaroslav
Ústav informatiky, 2016

Sparse robust portfolio optimization via NLP regularizations
Branda, Martin; Červinka, Michal; Schwartz, A.
2016 - anglický We deal with investment problems where we minimize a risk measure under a condition on the sparsity of the portfolio. Various risk measures are considered including Value-at-Risk and Conditional Value-at-Risk under normal distribution of returns and their robust counterparts are derived under moment conditions, all leading to nonconvex objective functions. We propose four solution approaches: a mixed-integer formulation, a relaxation of an alternative mixed-integer reformulation and two NLP regularizations. In a numerical study, we compare their computational performance on a large number of simulated instances taken from the literature. We deal with investment problems where we minimize a risk measure\nunder a condition on the sparsity of the portfolio. Various risk measures\nare considered including Value-at-Risk and Conditional Value-at-Risk\nunder normal distribution of returns and their robust counterparts are\nderived under moment conditions, all leading to nonconvex objective\nfunctions. We propose four solution approaches: a mixed-integer formulation,\na relaxation of an alternative mixed-integer reformulation and\ntwo NLP regularizations. In a numerical study, we compare their computational\nperformance on a large number of simulated instances taken\nfrom the literature. Klíčová slova: Conditional Value-at-Risk; Value-at-Risk; risk measure Dokument je dostupný na externích webových stránkách. Sparse robust portfolio optimization via NLP regularizations

We deal with investment problems where we minimize a risk measure under a condition on the sparsity of the portfolio. Various risk measures are considered including Value-at-Risk and Conditional ...

Branda, Martin; Červinka, Michal; Schwartz, A.
Ústav teorie informace a automatizace, 2016

New Quasi-Newton Method for Solving Systems of Nonlinear Equations
Lukšan, Ladislav; Vlček, Jan
2016 - anglický Klíčová slova: nonlinear equations; systems of equations; trust-region methods; quasi-Newton methods; adjoint Broyden methods; numerical algorithms; numerical experiments Plné texty jsou dostupné v digitálním repozitáři NUŠL New Quasi-Newton Method for Solving Systems of Nonlinear Equations

Lukšan, Ladislav; Vlček, Jan
Ústav informatiky, 2016

Neural Networks Between Integer and Rational Weights
Šíma, Jiří
2016 - anglický The analysis of the computational power of neural networks with the weight parameters between integer and rational numbers is refined. We study an intermediate model of binary-state neural networks with integer weights, corresponding to finite automata, which is extended with an extra analog unit with rational weights, as already two additional analog units allow for Turing universality. We characterize the languages that are accepted by this model in terms of so-called cut languages which are combined in a certain way by usual string operations. We employ this characterization for proving that the languages accepted by neural networks with an analog unit are context-sensitive and we present an explicit example of such non-context-free languages. In addition, we formulate a sufficient condition when these networks accept only regular languages in terms of quasi-periodicity of parameters derived from their weights. Klíčová slova: neural networks; analog unit; rational weight; cut languages; computational power Plné texty jsou dostupné v digitálním repozitáři NUŠL Neural Networks Between Integer and Rational Weights

The analysis of the computational power of neural networks with the weight parameters between integer and rational numbers is refined. We study an intermediate model of binary-state neural networks ...

Šíma, Jiří
Ústav informatiky, 2016

Detection of Differential Item Functioning with Non-Linear Regression: Non-IRT Approach Accounting for Guessing
Drabinová, Adéla; Martinková, Patrícia
2016 - anglický In this article, we present a new method for estimation of Item Response Function and for detection of uniform and non-uniform Differential Item Functioning (DIF) in dichotomous items based on Non-Linear Regression (NLR). Proposed method extends Logistic Regression (LR) procedure by including pseudoguessing parameter. NLR technique is compared to LR procedure and Lord’s and Raju’s statistics for three-parameter Item Response Theory (IRT) models in simulation study based on Graduate Management Admission Test. NLR shows superiority in power at low rejection rate over IRT methods and outperforms LR procedure in power for case of uniform DIF detection. Our research suggests that the newly proposed non-IRT procedure is an attractive and user friendly approach to DIF detection. Klíčová slova: differential item functioning; non-linear regression; logistic regression; item response theory Plné texty jsou dostupné v digitálním repozitáři NUŠL Detection of Differential Item Functioning with Non-Linear Regression: Non-IRT Approach Accounting for Guessing

In this article, we present a new method for estimation of Item Response Function and for detection of uniform and non-uniform Differential Item Functioning (DIF) in dichotomous items based on ...

Drabinová, Adéla; Martinková, Patrícia
Ústav informatiky, 2016