Institute for Competitiveness and Communication
Dauerhafte URI für die Sammlunghttps://irf.fhnw.ch/handle/11654/62
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Ergebnisse nach Hochschule und Institut
Publikation Robustification of the Quintile Share Ratio(26.06.2009) Hulliger, Beat; Schoch, Tobias06 - PräsentationPublikation Policy Recommendations and Methodological Report. Research Project Report WP10, D10.1/D10.2, FP7-SSH-2007-217322 AMELI(01.03.2011) Münnich, Ralf; Zins, Stefan; Alfons, Andreas; Bruch, Christian; Filzmoser, Peter; Monique, Graf; Hulliger, Beat; Kolb, Jan-Philipp; Lehtonen, Risto; Lussmann Pooda, Daniela; Meraner, Angelika; Myrskylä, Mirko; Nedyalkova, Desislava; Schoch, Tobias; Templ, Matthias; Valaste, Maria; Veijanen, Ari05 - Forschungs- oder ArbeitsberichtPublikation Robust Unit-Level Small Area Estimation: A Fast Algorithm for Large Datasets(Austrian Journal of Statistics, 01.12.2011) Schoch, TobiasSmall area estimation is a topic of increasing importance in official statistics. Although the classical EBLUP method is useful for estimating the small area means efficiently under the normality assumptions, it can be highly influenced by the presence of outliers. Therefore, Sinha and Rao (2009; The Canadian Journal of Statistics) proposed robust estimators/predictors for a large class of unit- and area-level models. We confine attention to the basic unit-level model and discuss a related, but slightly different, robustification. In particular, we develop a fast algorithm that avoids inversion and multiplication of large matrices, and thus permits the user to apply the method to large datasets. In addition, we derive much simpler expressions of the bounded-influence predicting equations to robustly predict the small-area means than Sinha and Rao (2009) did.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Mechanisms for multivariate outliers and missing values(19.06.2013) Hulliger, Beat; Schoch, Tobias04B - Beitrag KonferenzschriftPublikation Robust Estimation for Poverty- and Inequality-Indicators(01.10.2010) Hulliger, Beat; Schoch, Tobias04B - Beitrag KonferenzschriftPublikation Social inequality and the biological standard of living: An anthropometric analysis of Swiss conscription data, 18751950(Elsevier, 01.05.2011) Schoch, Tobias; Staub, Kaspar; Pfister, Christian01A - Beitrag in wissenschaftlicher ZeitschriftPublikation State-of-the-art of Laeken Indicators. Research Project Report WP1, D1.1, FP7-SSH-2007-217322 AMELI(01.03.2011) Monique, Graf; Alfons, Andreas; Bruch, Christian; Filzmoser, Peter; Hulliger, Beat; Lehtonen, Risto; Meindl, Bernhard; Münnich, Ralf; Schoch, Tobias; Templ, Matthias; Valaste, Maria; Wenger, Ariane; Zins, Stefan05 - Forschungs- oder ArbeitsberichtPublikation Robust Methodology for Laeken Indicators. Research Project Report WP4, D4.2, FP7-SSH-2007-217322 AMELI(01.03.2011) Hulliger, Beat; Alfons, Andreas; Filzmoser, Peter; Meraner, Angelika; Schoch, Tobias; Templ, Matthias05 - Forschungs- oder ArbeitsberichtPublikation Robust Multivariate Methods for Income Data(26.08.2011) Hulliger, Beat; Schoch, TobiasWith the EU Statistics on Income and Living Conditions (EU-SILC), the European Union established a coordinated survey and adopted a set of indicators (Laeken indicators) to monitor poverty and social cohesion. In particular, the monetary Laeken indicators are based on the equivalized disposable income per person, an aggregation and redistribution of person- and household-specific income components (e.g., income from employment and capital; unemployment-, old-age-, survivors'-, and disability benefits, etc.). To understand this highly complex data the components that are exclusively measured at household-level are distributed among the household members while the individual components are investigated before they are aggregated and redistributed to all household members. The personal income components show the following characteristics: the marginal distribution of each component is heavily skewed and has a remarkable point mass at zero, the joint distribution of the components is far from being elliptically contoured (even after appropriate transformation), an overwhelming majority of observations lies on subspaces i.e., exhibits structural zeros on certain dimension (e.g., individuals on working age with a positive employee-cash income do neither receive old-age nor unemployment benefits, and vice versa), within subspaces the observations are clustered with respect to non-monetary, socio-economic characteristics, many components have missing values, and finally there are outliers in many components but in addition there are genuinely multivariate outliers. The influence of outliers and outlier treatments on the components and on the equivalized disposable income and the Laeken indicators are investigated. In particular the outliers may have a considerable effect on the the Laeken indicators. The presentation shows the development of outlier detection and imputation methods which are capable to treat the structural zeros appropriately, which work with missing values, which cope with the complex nature of the data, which take the sampling design into account, and which are still computationally feasible.04B - Beitrag KonferenzschriftPublikation Robust Multivariate Methods for Income Data(Eurostat, 01.02.2011) Hulliger, Beat; Schoch, TobiasIncome inequality and poverty measures are central to the analysis of social welfare. However, recording and measurement errors, outlying observations exert strong influence on non-robust estimators of these measures. If the data cannot be purged of these, welfare conclusions drawn from the data can be seriously misleading. Moreover, these measures are computed on the basis of a univariate income variable, which is an aggregation of several distinct income sources or components. Notably outliers in several income components may severely affect the univariate income variable and thus the estimates. In addition, the aggregation process may propagate or mask outliers in the components. Therefore, instead of focusing on univariate robust estimators, propose to adopt truly multivariate outlier-detection and robust imputation methods. Both, outlier-detection- and imputation methods are adapted for the finite population sampling context and can cope with missing values and the multiple zero-inflation structure of income data. This kind of data.04B - Beitrag Konferenzschrift
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