| LDR | | 02006nmm uu200397 4500 |
| 001 | | 000000334176 |
| 005 | | 20240805175300 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438177727 |
| 035 | |
▼a (MiAaPQ)AAI10828657 |
| 035 | |
▼a (MiAaPQ)washington:18901 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Weihs, Luca. |
| 245 | 10 |
▼a Parameter Identification and Assessment of Independence in Multivariate Statistical Modeling. |
| 260 | |
▼a [S.l.] :
▼b University of Washington.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 123 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Mathias Drton. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Washington, 2018. |
| 520 | |
▼a We are interested in the extent to which, possibly causal, relationships can be statistically quantified from multivariate data obtained from a system of random variables. In the ideal setting, we would begin with refined knowledge of which vari |
| 520 | |
▼a While scientists may not always be able to conduct a controlled experiment, thus only having observational data, they may they may be able to hypothesize or determine the directions in which causal relations point. For instance, ``mother smoking |
| 520 | |
▼a Departing even further from the above ideal, a scientist may be in the exploratory stage of research and thus have little to no understanding of the causal or functional relationships in their data. In this case, a natural first question to ask |
| 590 | |
▼a School code: 0250. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a University of Washington.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0250 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999198
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |