Liebe Studierende,

am 05.12.2025 findet wieder der jährliche EMOS-Tag im Bayerischen Landesamt für Statistik in Fürth statt. Wir laden Sie herzlich zur Teilnahme ein. Auch in diesem Jahr erwarten Sie wieder spannende Themen rund um die amtliche Statistik.

Was erwartet Sie?

• Praktikumsangebote
• Vorstellung externer Abschlussarbeitsthemen
• Einblicke in die Arbeit des Landesamtes und in die amtliche Statistik

Wer wir sind:

• Zentraler Informationsdienstleister für die amtliche Statistik in Bayern
• Unsere Hauptaufgabe ist die Erhebung und Aufbereitung gesetzlich angeordneter Statistiken in verschiedenen Themenfeldern

Die Teilnahme ist kostenfrei. Fahrtkosten können über Sammeltickets erstattet werden.
Bitte melden Sie sich bis zum 30.10.2025 unter folgender E-Mail-Adresse zur Teilnahme an.

silvia.foertsch(at)uni-bamberg.de

Der EMOS-Tag findet nur bei ausreichender Zahl an Teilnehmenden statt.

Wir heißen Johanna Einhorn herzlich willkommen am Lehrstuhl.

Frink, N.; Schmid, T. 

Abstract: Small area estimation methods are proposed that use generalized tree-based machine learning techniques to improve the estimation of disaggregated means in small areas using discrete survey data. Specifically, two existing approaches based on random forests - the Generalized Mixed Effects Random Forest (GMERF) and a Mixed Effects Random Forest (MERF) - are extended to accommodate count outcomes, addressing key challenges such as overdispersion. Additionally, three bootstrap methodologies designed to assess the reliability of point estimators for area-level means are evaluated. The numerical analysis shows that the MERF, which does not assume a Poisson distribution to model the mean behavior of count data, excels in scenarios of severe overdispersion. Conversely, the GMERF performs best under conditions where Poisson distribution assumptions are moderately met. In a case study using real-world data from the state of Guerrero, Mexico, the proposed methods effectively estimate area-level means while capturing the uncertainty inherent in overdispersed count data. These findings highlight their practical applicability for small area estimation.

Nicolas Frink & Timo Schmid (2025) Small area prediction of counts under machine learning-type mixed models, Computational Statistics & Data Analysis, DOI: https://doi.org/10.1016/j.csda.2025.108218

Krennmair, P.; Würz, N.; Schmid, T. 

Abstract: Evidence-based policy-making requires reliable, spatially disaggregated indicators. The framework of mixed effects random forests leverages the advantages of random forests and hierarchical data in small area estimation. These methods require typically access to auxiliary information on population level, which is a strong limitation for practitioners. In contrast, our proposed method—for point and uncertainty estimation—abstains from access to unit-level population data but adaptively incorporates aggregated auxiliary information through calibration weights. We demonstrate its usage for estimating opportunity cost of care work for Germany from the Socio-Economic Panel and census aggregates. Simulation studies evaluate our proposed method.

Patrick Krennmair, Nora Würz & Timo Schmid (2025) Analysing opportunity cost of care work using mixed effects random forests under aggregated auxiliary data, Journal of the Royal Statistical Society Series C, DOI: https://doi.org/10.1093/jrsssc/qlaf031