| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000435378 |
| 005 | | 20200228093934 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085582063 |
| 035 | |
▼a (MiAaPQ)AAI13810960 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Khim, Justin Turner. |
| 245 | 10 |
▼a Testing Infection Graphs. |
| 260 | |
▼a [S.l.]:
▼b University of Pennsylvania.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 116 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-02, Section: B. |
| 500 | |
▼a Advisor: Ma, Zongming. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Pennsylvania, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a We study the following problem: given two graphs G0 and G1 defined on a common set of n vertices and a single observation of the statuses of these vertices, i.e. either infected, uninfected, or censored, did the infection spread on G0 or G1? Modern instances of such ``infections'' include diseases such as HIV, behaviors such as smoking, or information such as online news articles. For particular stochastic spreading mechanisms, we give algorithms for this testing problem based on hypothesis discretization and permutation-invariance. Additionally, these methods also lead to confidence sets for parameters that also govern the spread of infection and for the graphs on which the infection spread. |
| 590 | |
▼a School code: 0175. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a University of Pennsylvania.
▼b Statistics. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-02B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0175 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15490670
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |