| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000330469 |
| 005 | | 20241031185111 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438371569 |
| 035 | |
▼a (MiAaPQ)AAI10845443 |
| 035 | |
▼a (MiAaPQ)uchicago:14524 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Ha, Wooseok.
▼0 (orcid)0000-0002-9069-854X |
| 245 | 10 |
▼a High-Dimensional Estimation and Optimization With Multiple Structured Signals. |
| 260 | |
▼a [S.l.] :
▼b The University of Chicago.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 175 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B. |
| 500 | |
▼a Adviser: Rina Foygel Barber. |
| 502 | 1 |
▼a Thesis (Ph.D.)--The University of Chicago, 2018. |
| 520 | |
▼a Statistical recovery in high-dimensional statistics and signal processing often requests a determination of multiple structured signals from massive data. And depending on its application, either one or both of the signals may be of primarily i |
| 520 | |
▼a Chapter 2 describes a low-rank + sparse decomposition problem under data compression and we study rigorous statistical performance guarantee that is achievable using a joint convex optimization based estimator. It is well known that the convex r |
| 590 | |
▼a School code: 0330. |
| 650 | 4 |
▼a Statistics. |
| 650 | 4 |
▼a Medical imaging. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0463 |
| 690 | |
▼a 0574 |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a The University of Chicago.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-02B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0330 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000061
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |