On a Low-Rank Matrix Single-Index Model
In this paper, we conduct a theoretical examination of a low-rank matrix single-index model. This model has recently been introduced in the field of biostatistics, but its theoretical properties for jointly estimating the link function and the coefficient matrix have not yet been fully explored. In...
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| Format: | Article |
| Language: | English |
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MDPI
2023
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| Online Access: | View Fulltext in Publisher View in Scopus |
| LEADER | 01353nam a2200205Ia 4500 | ||
|---|---|---|---|
| 001 | 10.3390-math11092065 | ||
| 008 | 230529s2023 CNT 000 0 und d | ||
| 020 | |a 22277390 (ISSN) | ||
| 245 | 1 | 0 | |a On a Low-Rank Matrix Single-Index Model |
| 260 | 0 | |b MDPI |c 2023 | |
| 856 | |z View Fulltext in Publisher |u https://doi.org/10.3390/math11092065 | ||
| 856 | |z View in Scopus |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159176360&doi=10.3390%2fmath11092065&partnerID=40&md5=c475c9ee7fa7dc9459403d5aaa7e6f9e | ||
| 520 | 3 | |a In this paper, we conduct a theoretical examination of a low-rank matrix single-index model. This model has recently been introduced in the field of biostatistics, but its theoretical properties for jointly estimating the link function and the coefficient matrix have not yet been fully explored. In this paper, we make use of the PAC-Bayesian bounds technique to provide a thorough theoretical understanding of the joint estimation of the link function and the coefficient matrix. This allows us to gain a deeper insight into the properties of this model and its potential applications in different fields. © 2023 by the author. | |
| 650 | 0 | 4 | |a low-rank matrix |
| 650 | 0 | 4 | |a optimal rate |
| 650 | 0 | 4 | |a oracle inequality |
| 650 | 0 | 4 | |a PAC-Bayes bounds |
| 650 | 0 | 4 | |a single-index model |
| 700 | 1 | 0 | |a Mai, T.T. |e author |
| 773 | |t Mathematics | ||