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How to down-weight observations in robust regression: A metalearning study
Kalina, Jan ; Pitra, Z.
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.
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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.
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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.
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Využití numerické lineární algebry k urychlení výpočtu odhadů MCD
Sommerová, Kristýna ; Duintjer Tebbens, Erik Jurjen (vedoucí práce) ; Hnětynková, Iveta (oponent)
Práce se zabývá urychlením algoritmizace estimátoru MCD pro odhad střední hodnoty a varianční matice normálně rozdělených mnohorozměrných dat zatíže- ných odlehlými hodnotami. Rozvádí nejprve myšlenku estimátoru a jeho známou aproximaci - algoritmus FastMCD. Důraz práce měl být především kladen na možné urychlení přímo iteračního kroku zvaného C-step ve FastMCD při zacho- vání kvality odhadů estimátoru. To se ukazálo přinejmenším jako obtížné. Práce se proto zaměřuje především na novou implementaci založenou na C-stepu a Ja- cobiho metodě pro vlastní čísla. Navrhovaný JacobiMCD je porovnán s FastMCD co do počtu operací a získávaných výsledků. Na závěr konstatuje, že JacobiMCD není přímo ekvivalentní s FastMCD, ale je možné ho použít na data velkých roz- měrů, kde z numerických experimentů vyplývá urychlení výpočtů o řád, přičemž kvalita výsledku se za určitého nastavení řádově blíží FastMCD. 1
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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.
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Exact Inference In Robust Econometrics under Heteroscedasticity
Kalina, Jan ; Peštová, B.
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. Theoretical results may be simply extended to the context of multivariate quantiles
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Výpočet a aplikace MCD estimátoru pro robustní statistické analýzy
Sommerová, Kristýna ; Duintjer Tebbens, Erik Jurjen (vedoucí práce) ; Hnětynková, Iveta (oponent)
Tato práce popisuje jeden ze základních problémů robustní statistiky, který spočívá v detekci odlehlých hodnot, a jeho možné řešení pomocí Minimum covariance determinant estimátoru pro odhad střední hodnoty a varianční matice mnohorozměrných dat. Vysvětluje fungování tohoto estimátoru a zkoumá jeho vlastnosti. Zaměřuje se pak především na aproximaci pomocí algoritmu fastMCD, pro který upřesňuje numerické vlastnosti s důrazem na výpočtovou náročnost a stabilitu ve standardní implementaci v MATLABu. Diskutuje také možné úpravy algoritmu a jejich vliv na numerické vlastnosti. Na závěr na několika experimentech s reálnými daty ukazuje použítí fastMCD algoritmu. Powered by TCPDF (www.tcpdf.org)
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Exact Inference In Robust Econometrics under Heteroscedasticity
Kalina, Jan ; Peštová, Barbora
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. Theoretical results may be simply extended to the context of multivariate quantiles
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Autocorrelated residuals of robust regression
Kalina, Jan
The work is devoted to the Durbin-Watson test for robust linear regression methods. First we explain consequences of the autocorrelation of residuals on estimating regression parameters. We propose an asymptotic version of the Durbin-Watson test for regression quantiles and trimmed least squares and derive an asymptotic approximation to the exact null distribution of the test statistic, exploiting the asymptotic representation for both regression estimators. Further, we consider the least weighted squares estimator, which is a highly robust estimator based on the idea to down-weight less reliable observations. We compare various versions of the Durbin-Watson test for the least weighted squares estimator. The asymptotic test is derived using two versions of the asymptotic representation. Finally, we investigate a weighted Durbin-Watson test using the weights determined by the least weighted squares estimator. The exact test is described and also an asymptotic approximation to the distribution of the weighted statistic under the null hypothesis is obtained.
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