Schalk, Daniel; Bischl, Bernd
ORCID: https://orcid.org/0000-0001-6002-6980 und Rügamer, David ORCID: https://orcid.org/0000-0002-8772-9202
(2024):
Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models.
In: Statistics and Computing, Vol. 34, No. 1, 31
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| Item Type: | Journal article |
|---|---|
| Faculties: | Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP) Mathematics, Computer Science and Statistics > Statistics |
| Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems 300 Social sciences > 300 Social sciences, sociology and anthropology |
| URN: | urn:nbn:de:bvb:19-epub-116545-6 |
| ISSN: | 0960-3174 |
| Language: | English |
| Item ID: | 116545 |
| Date Deposited: | 08. May 2024 14:03 |
| Last Modified: | 08. May 2024 14:03 |
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