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Bayesian Selection Model With Shrinking Priors for Nonignorable Missingness- [electronic resource]
Bayesian Selection Model With Shrinking Priors for Nonignorable Missingness- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101913
- ISBN
- 9798380360999
- DDC
- 150
- 서명/저자
- Bayesian Selection Model With Shrinking Priors for Nonignorable Missingness - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Los Angeles., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(208 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- 주기사항
- Advisor: Enders, Craig K.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약This study investigates the effectiveness of Bayesian variable selection (BVS) procedures in dealing with missing not at random (MNAR) data for identification in selection models. Three BVS-adapted selection models, namely Bayesian LASSO, horseshoe prior, and spike-and-slab prior, were compared, along with established missing data methods such as a model that assumes a missing at random (MAR) process and full-selection model. The results indicate that the spike-and-slab prior consistently outperformed other BVS methods in terms of accuracy and bias for various parameters, including slope estimates, residual variance, and intercept. When compared with the full-selection model, the spike-and-slab model exhibited superior performance across all parameters based on mean squared error (MSE) results.Although the MAR and spike-and-slab models showed comparable performance for slope estimates, the spike-and-slab model consistently outperformed the MAR model in estimating residual variance and intercept. This comparable performance is attributed to the bias-variance tradeoff. The MAR model, while biased, demonstrated efficiency by estimating fewer parameters than selection models and obtaining robust support from the observed data. On the other hand, the spike-and-slab model outperformed the full-selection model, even when the full-selection model aligned with the true data-generating model. The adaptation of BVS to selection models, particularly through the spike-and-slab method, yielded promising results with unbiased estimates under various conditions. However, it is important to acknowledge that this study represents an initial exploration of this subject, and its scope was inherently limited. Finally, the BVS adaptations to the selection model was illustrated with data from a clinical-trial study.
- 일반주제명
- Psychology.
- 일반주제명
- Statistics.
- 일반주제명
- Mathematics.
- 키워드
- Horseshoe
- 키워드
- Slope estimates
- 기타저자
- University of California, Los Angeles Psychology 0780
- 기본자료저록
- Dissertations Abstracts International. 85-03B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101913
■006m o d
■007cr#unu||||||||
■020 ▼a9798380360999
■035 ▼a(MiAaPQ)AAI30687279
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a150
■1001 ▼aVera, Juan Diego.
■24510▼aBayesian Selection Model With Shrinking Priors for Nonignorable Missingness▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, Los Angeles. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(208 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-03, Section: B.
■500 ▼aAdvisor: Enders, Craig K.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThis study investigates the effectiveness of Bayesian variable selection (BVS) procedures in dealing with missing not at random (MNAR) data for identification in selection models. Three BVS-adapted selection models, namely Bayesian LASSO, horseshoe prior, and spike-and-slab prior, were compared, along with established missing data methods such as a model that assumes a missing at random (MAR) process and full-selection model. The results indicate that the spike-and-slab prior consistently outperformed other BVS methods in terms of accuracy and bias for various parameters, including slope estimates, residual variance, and intercept. When compared with the full-selection model, the spike-and-slab model exhibited superior performance across all parameters based on mean squared error (MSE) results.Although the MAR and spike-and-slab models showed comparable performance for slope estimates, the spike-and-slab model consistently outperformed the MAR model in estimating residual variance and intercept. This comparable performance is attributed to the bias-variance tradeoff. The MAR model, while biased, demonstrated efficiency by estimating fewer parameters than selection models and obtaining robust support from the observed data. On the other hand, the spike-and-slab model outperformed the full-selection model, even when the full-selection model aligned with the true data-generating model. The adaptation of BVS to selection models, particularly through the spike-and-slab method, yielded promising results with unbiased estimates under various conditions. However, it is important to acknowledge that this study represents an initial exploration of this subject, and its scope was inherently limited. Finally, the BVS adaptations to the selection model was illustrated with data from a clinical-trial study.
■590 ▼aSchool code: 0031.
■650 4▼aPsychology.
■650 4▼aStatistics.
■650 4▼aMathematics.
■653 ▼aBayesian variable selection
■653 ▼aHorseshoe
■653 ▼aMean squared error
■653 ▼aSlope estimates
■690 ▼a0621
■690 ▼a0405
■690 ▼a0463
■71020▼aUniversity of California, Los Angeles▼bPsychology 0780.
■7730 ▼tDissertations Abstracts International▼g85-03B.
■773 ▼tDissertation Abstract International
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16935279▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
