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

On Nominal Automata as Models of Java-like Object-Oriented Programs
Suzuki, Tomoyuki
2016 - anglický In this paper, we proposed a model of Java-like object-oriented programs as nominal automata and a simple method invocation checker. Plné texty jsou dostupné na vyžádání prostřednictvím repozitáře Akademie věd. On Nominal Automata as Models of Java-like Object-Oriented Programs

In this paper, we proposed a model of Java-like object-oriented programs as nominal automata and a simple method invocation checker.

Suzuki, Tomoyuki
Ústav informatiky, 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

Discerning Two Words by a Minimum Size Automaton
Wiedermann, Jiří
2016 - anglický Klíčová slova: finite automaton; discerning two words; complexity Plné texty jsou dostupné v digitálním repozitáři NUŠL Discerning Two Words by a Minimum Size Automaton

Wiedermann, Jiří
Ústav informatiky, 2016

Report on the Last Work by Dr. Erich Nuding
Rohn, Jiří
2016 - anglický This is a facsimile copy of a 1994 report on the unpublished last paper by Dr. Erich Nuding. It is being made public here in the hope that even after twenty-two years it may be of interest for researchers working in the area of interval computations because of the intriguing concept of the "fourth modality" which has not been rediscovered during a quarter of century which has elapsed since its original formulation. Klíčová slova: set-valued mapping; interval linear equations; solution set; fourth modality Plné texty jsou dostupné v digitálním repozitáři NUŠL Report on the Last Work by Dr. Erich Nuding

This is a facsimile copy of a 1994 report on the unpublished last paper by Dr. Erich Nuding. It is being made public here in the hope that even after twenty-two years it may be of interest for ...

Rohn, Jiří
Ústav informatiky, 2016

Measures for Classification Results Evaluation
Řezanková, Hana; Húsek, Dušan
2015 - anglický Klíčová slova: similarity measures; measures of agreement; success rate of classification Plné texty jsou dostupné v digitálním repozitáři NUŠL Measures for Classification Results Evaluation

Řezanková, Hana; Húsek, Dušan
Ústav informatiky, 2015

Causation Entropy Principle and Bayesian Inference to Causal Networks
Coufal, David; Hlinka, Jaroslav
2015 - anglický Klíčová slova: causal links; causation entropy; Bayesian inference Plné texty jsou dostupné v digitálním repozitáři NUŠL Causation Entropy Principle and Bayesian Inference to Causal Networks

Coufal, David; Hlinka, Jaroslav
Ústav informatiky, 2015