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Titel:

Statistical Significance in High-dimensional Linear Mixed Models

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Lin, Lina; Drton, Mathias; Shojaie, Ali
Seitenangaben Beitrag:
171-181
Abstract:
This paper develops an inferential framework for high-dimensional linear mixed effect models. Such models are suitable, e.g., when collecting n repeated measurements for M subjects. We consider a scenario where the number of fixed effects p is large (and may be larger than M), but the number of random effects q is small. Our framework is inspired by a recent line of work that proposes de-biasing penalized estimators to perform inference for high-dimensional linear models with fixed effects only....     »
Dewey-Dezimalklassifikation:
510 Mathematik
Kongress- / Buchtitel:
Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference
Datum der Konferenz:
October 2020
Verlag / Institution:
Association for Computing Machinery
Verlagsort:
New York, NY
Publikationsdatum:
18.10.2020
Jahr:
2020
Quartal:
4. Quartal
Jahr / Monat:
2020-10
Monat:
Oct
Print-ISBN:
9781450381031
Sprache:
en
Erscheinungsform:
Print
Volltext / DOI:
doi:10.1145/3412815.3416883
WWW:
FODS ’20, October 19–20, 2020, Virtual Event, USA
Semester:
WS 20-21
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
Text
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