Robust estimation with survey data
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Publikationsdatum
26.11.2025
Typ der Arbeit
Studiengang
Typ
06 - Präsentation
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Übergeordnetes Werk
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Bucharest
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Zusammenfassung
Outlier detection and handling is a non-trivial task, even when the data are regarded as a random sample from an infinite population. In this context (i.e., classical statistics), outliers are typically considered to be generated by a model other than the one under study. Compared to classical statistics, outliers are a very different concept in finite population sampling. In the context of sampling (design-based inference), where no statistical model is assumed, outliers are extreme values that deviate from the bulk of the data. In addition, unlike in classical statistics, we also have to consider the sampling weights. Observations that are not considered outliers (i.e., that are in the bulk of the data) can still strongly influence an estimator due to their large sampling weight (influential values).
An estimator or procedure is called (qualitatively) robust if it is resistant or insensitive to the presence of outliers and influential values. In principle, robust estimation can be implemented in two ways: i) detection and treatment of outliers, or ii) direct application of robust estimation techniques. We limit our attention to the latter approach. The robsurvey package implements: i) basic robust estimators of the mean and total (e.g., robust Horvitz-Thompson estimator), robust survey regression, and model-assisted estimation (e.g., robust generalized regression estimator, GREG). In the talk, we will take a look at some of the methods and illustrate them with examples from business surveys.
Schlagwörter
robust statistics, sampling, outlier detection
Fachgebiet (DDC)
Veranstaltung
uRos 2025
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
24.11.2025
Enddatum der Konferenz
26.11.2025
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Begutachtung
Peer-Review des Abstracts
Open Access-Status
Lizenz
Zitation
Schoch, T. (2025, November 26). Robust estimation with survey data. uRos 2025. https://doi.org/10.26041/fhnw-14422