| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000434632 |
| 005 | | 20200226161616 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781687955852 |
| 035 | |
▼a (MiAaPQ)AAI22621303 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 574 |
| 100 | 1 |
▼a Sondhi, Arjun. |
| 245 | 10 |
▼a Statistical Miscellany: Causality, Networks, and Bandits. |
| 260 | |
▼a [S.l.]:
▼b University of Washington.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 144 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Shojaie, Ali. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Washington, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 506 | |
▼a This item must not be added to any third party search indexes. |
| 520 | |
▼a In this dissertation, we make methodological contributions in three separate areas. In Chapter 2, we introduce a new algorithm for learning high-dimensional causal networks from observational data. Our algorithm, which is a simple modification to the well-known PC-Algorithm, provides reductions in both computational and sample complexity, by leveraging properties of common random graph families. In Chapter 3, we develop a penalized regression framework to integrate known network structure into high-dimensional generalized linear models. Our framework is unique in that it considers two-way structured data, where networks connect both the features and the observation units. We also introduce a statistical inference procedure to provide valid confidence intervals and hypothesis tests. Finally, in Chapter 4, we present an improved estimator for counterfactual policy evaluation in contextual bandit problems. This method is based on classifier-based density ratio estimation, and displays state-of-the-art performance for continuous action spaces. We conclude with a discussion in Chapter 5, describing the limitations of the work, and avenues for future research. |
| 590 | |
▼a School code: 0250. |
| 650 | 4 |
▼a Biostatistics. |
| 690 | |
▼a 0308 |
| 710 | 20 |
▼a University of Washington.
▼b Biostatistics - Public Health. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0250 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493801
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |