Ústav informatiky

Nejnovější přírůstky:
2018-12-07
17:59
Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators
Kalina, Jan
The paper is devoted to highly robust statistical estimators based on implicit weighting, which have a potential to find econometric applications. Two particular methods include a robust correlation coefficient based on the least weighted squares regression and the minimum weighted covariance determinant estimator, where the latter allows to estimate the mean and covariance matrix of multivariate data. New tools are proposed allowing to test hypotheses about these robust estimators or to estimate their variance. The techniques considered in the paper include resampling approaches with or without replacement, i.e. permutation tests, bootstrap variance estimation, and bootstrap confidence intervals. The performance of the newly described tools is illustrated on numerical examples. They reveal the suitability of the robust procedures also for non-contaminated data, as their confidence intervals are not much wider compared to those for standard maximum likelihood estimators. While resampling without replacement turns out to be more suitable for hypothesis testing, bootstrapping with replacement yields reliable confidence intervals but not corresponding hypothesis tests.

Úplný záznam
2018-12-07
17:59
Robust Metalearning: Comparing Robust Regression Using A Robust Prediction Error
Peštová, Barbora ; Kalina, Jan
The aim of this paper is to construct a classification rule for predicting the best regression estimator for a new data set based on a database of 20 training data sets. Various estimators considered here include some popular methods of robust statistics. The methodology used for constructing the classification rule can be described as metalearning. Nevertheless, standard approaches of metalearning should be robustified if working with data sets contaminated by outlying measurements (outliers). Therefore, our contribution can be also described as robustification of the metalearning process by using a robust prediction error. In addition to performing the metalearning study by means of both standard and robust approaches, we search for a detailed interpretation in two particular situations. The results of detailed investigation show that the knowledge obtained by a metalearning approach standing on standard principles is prone to great variability and instability, which makes it hard to believe that the results are not just a consequence of a mere chance. Such aspect of metalearning seems not to have been previously analyzed in literature.

Úplný záznam
2018-11-15
12:40
Stav předaných dat a úprava rozdělení vybraných měření 2018
Novák, Jakub ; Jiřina, M. ; Benešová, Michaela
Zpráva se týká stavu předávaných dat a úpravy rozdělení vybraných měření pro rok 2018 v rámci projektu TDD-ČR. Cílem je informovat o aktuálním stavu dat a navrhnout opatření pro zachování reprezentativity celého vzorku. Jsou uvedena kritéria filtrace dat a navrženy postupy pro přerozdělení vybraných měření.

Úplný záznam
2018-11-15
12:40
Problems for Nonlinear Least Squares and Nonlinear Equations
Lukšan, Ladislav ; Matonoha, Ctirad ; Vlček, Jan
This report contains a description of subroutines which can be used for testing large-scale optimization codes. These subroutines can easily be obtained from the web page http://www.cs.cas.cz/~luksan/test.html. Furthermore, all test problems contained in these subroutines are presented in the analytic form.
Plný text: Stáhnout plný textPDF

Úplný záznam
2018-11-15
12:40
Sparse Test Problems for Nonlinear Least Squares
Lukšan, Ladislav ; Matonoha, Ctirad ; Vlček, Jan
This report contains a description of subroutines which can be used for testing large-scale optimization codes. These subroutines can easily be obtained from the web page http://www.cs.cas.cz/~luksan/test.html. Furthermore, all test problems contained in these subroutines are presented in the analytic form.
Plný text: Stáhnout plný textPDF

Úplný záznam
2018-10-02
14:38
How to down-weight observations in robust regression: A metalearning study
Kalina, Jan ; Pitra, Zbyněk
Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is becoming popular in statistical learning and there is an increasing number of metalearning applications also in the analysis of economic data sets. Still, not much attention has been paid to its limitations and disadvantages. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 30 data sets with economic background and perform a metalearning study over them as well as over the same data sets after an artificial contamination. We focus on comparing the prediction performance of the least weighted squares estimator with various weighting schemes. A broader spectrum of classification methods is applied and a support vector machine turns out to yield the best results. While results of a leave-1-out cross validation are very different from results of autovalidation, we realize that metalearning is highly unstable and its results should be interpreted with care. We also focus on discussing all possible limitations of the metalearning methodology in general.

Úplný záznam
2018-05-02
16:46
Various Approaches to Szroeter’s Test for Regression Quantiles
Kalina, Jan ; Peštová, Barbora
Regression quantiles represent an important tool for regression analysis popular in econometric applications, for example for the task of detecting heteroscedasticity in the data. Nevertheless, they need to be accompanied by diagnostic tools for verifying their assumptions. The paper is devoted to heteroscedasticity testing for regression quantiles, while their most important special case is commonly denoted as the regression median. Szroeter’s test, which is one of available heteroscedasticity tests for the least squares, is modified here for the regression median in three different ways: (1) asymptotic test based on the asymptotic representation for regression quantiles, (2) permutation test based on residuals, and (3) exact approximate test, which has a permutation character and represents an approximation to an exact test. All three approaches can be computed in a straightforward way and their principles can be extended also to other heteroscedasticity tests. The theoretical results are expected to be extended to other regression quantiles and mainly to multivariate quantiles.

Úplný záznam
2018-04-18
16:23

Úplný záznam
2018-04-06
16:29

Úplný záznam
2018-03-16
15:47

Úplný záznam