Schoch, Tobias
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Schoch, Tobias
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- Publikationrobsurvey: robust survey statistics estimation(23.06.2022) Schoch, TobiasThe robsurvey package provides robust estimation methods for data from complex sample surveys. The package implements the following methods: (1) basic outlier-robust location estimators of the population mean and total using weight reduction, trimming, winsorization, and M-estimation (robust Horvitz-Thompson and Hajek estimators); (2) robust survey regression M- and GM-estimators of the type Mallows and Schweppe; (3) robust model-assisted estimators of the population mean and total. A key design pattern of the package is that the methods are available in two flavors: bare-bone functions and survey methods. Bare-bone functions are stripped-down versions of the survey methods in terms of functionality. They may serve package developers as building blocks. The survey methods are much more capable and depend–for variance estimation–on the R package survey. The talk is organized into three parts: (1) Overview of the robust methods in robsurvey, including a comparison with other R packages (survey, robustbase, robeth, and MASS), Stata (robstat and rreg), SAS (robustreg), NAG and GNU Scientific Library. (2) Design patterns and possible extensions of the package. (3) Use cases and applications of the package.06 - Präsentation
- Publikationwbacon: Weighted BACON algorithms for multivariate outlier nomination (detection) and robust linear regression(Institute for Competitiveness and Communication, Hochschule für Wirtschaft FHNW, 15.06.2021) Schoch, TobiasThe BACON algorithms are methods for multivariate outlier nomination (detection) and robust linear regression by Billor, Hadi, and Velleman (2000, doi:10.1016/S0167-9473(99)00101-2). The extension to weighted problems is due to Beguin and Hulliger (2008, www150.statcan.gc.ca/n1/en/catalogue/12-001-X200800110616); see also Schoch (2021, doi:10.21105/joss.03238).09 - Software
- PublikationRobuste Schätzer für das Fay-Herriot-Modell(2019) Schoch, TobiasRobuste Methoden für die Small-Area-Schätzung von Mittel- und Totalwerten sind seit einiger Zeit bekannt und werden erfolgreich in der Praxis eingesetzt. Eine Vielzahl von «robustifizierten» SAE-Schätzern ist aus ad-hoc-Überlegungen entstanden, was deren Tauglichkeit nicht schmälert. Für die Robustifizierung von Schätzern zum Fay-Herriot-Modell nehmen wir eine «theorie-nahe» Perspektive ein, was zu neuen Einsichten führt. Fay-Herriot (1979, J Amer Stat Assoc) haben das nach ihnen benannte Modell als Verallgemeinerung des James-Stein-Schätzers motiviert, wobei sie sich einen empirischen Bayes-Ansatz zunutze machten. Wir greifen diese Motivation des Problems auf und formulieren ein analoges robustes Bayes’sches Verfahren. Wählt man nun in der Bayes’schen Problemformulierung die ungünstigste Verteilung (eng. least favorable distribution) von Huber (1964, Ann Math Statist) als A-priori-Verteilung für die Lokationswerte der Small Areas, dann resultiert als Bayes-Schätzer [= Schätzer mit06 - Präsentation
- PublikationEvaluation des kantonalen Durchimpfungsmonitorings Schweiz(Bundesamt für Gesundheit BAG, 2017) Hulliger, Beat; Schoch, Tobias; Walther, Ursula05 - Forschungs- oder Arbeitsbericht
- PublikationMechanisms for multivariate outliers and missing values(19.06.2013) Hulliger, Beat; Schoch, Tobias [in: NTTS - Conferences on New Techniques and Technologies for Statistics. Brussels, 5-7 March 2013, Proceedings]04B - Beitrag Konferenzschrift
- PublikationMechanisms for multivariate outliers and missing values(19.06.2013) Hulliger, Beat; Schoch, Tobias06 - Präsentation
- PublikationRobust, distribution-free inference for income share ratios under complex sampling(Springer, 26.05.2013) Hulliger, Beat; Schoch, Tobias [in: AStA Advances in Statistical Analysis]01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationRobust Unit-Level Small Area Estimation: A Fast Algorithm for Large Datasets(Austrian Journal of Statistics, 01.12.2011) Schoch, Tobias [in: Austrian Journal of Statistics]Small 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 Zeitschrift
- PublikationRobust 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 Konferenzschrift
- PublikationRobust High Breakdown Point Estimation in Unit-Level SAE Models(13.08.2011) Schoch, Tobias04B - Beitrag Konferenzschrift