User: Guest  Login
Document type:
Konferenzbeitrag 
Author(s):
Lin, Lina; Drton, Mathias; Shojaie, Ali 
Title:
Statistical Significance in High-dimensional Linear Mixed Models 
Pages contribution:
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 Decimal Classification:
510 Mathematik 
Book / Congress title:
Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference 
Date of congress:
October 2020 
Publisher:
Association for Computing Machinery 
Publisher address:
New York, NY 
Date of publication:
18.10.2020 
Year:
2020 
Quarter:
4. Quartal 
Year / month:
2020-10 
Month:
Oct 
Print-ISBN:
9781450381031 
Language:
en 
Publication format:
Print 
Fulltext / DOI:
Semester:
WS 20-21 
TUM Institution:
Lehrstuhl für Mathematische Statistik 
Format:
Text