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Problems in Variable Selection : = False Discovery Rate Control and Variational Inference.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Problems in Variable Selection :/
其他題名:	False Discovery Rate Control and Variational Inference.
作者:	Lin, Buyu.
面頁冊數:	1 online resource (316 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Biostatistics. -
電子資源:	click for full text (PQDT)
ISBN:	9798382777672

Problems in Variable Selection : = False Discovery Rate Control and Variational Inference.
Lin, Buyu.

Problems in Variable Selection :False Discovery Rate Control and Variational Inference. - 1 online resource (316 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Harvard University, 2024.

Includes bibliographical references

Variable selection plays a key role in modern high-dimensional statistics. This dissertation provides a comprehensive survey of theory and methods developed by the author and collaborators, with a specific focus on two areas: false discovery rate (FDR) control and Bayesian variable selection. In the domain of FDR control, we introduce a data-splitting method to asymptotically control the FDR while maintaining a high power. Furthermore, a Multiple Data Splitting (MDS) method is proposed to stabilize the selection result and boost the power. In Chapter 1, we apply both DS and MDS to the generalized linear models, which appear to be more robust in finite-sample cases compared to existing methods. Chapter 2 provides some following discussions regarding the proposed method and Chapter 3 compares the power of the proposed method with two existing methods: the model-X knockoff and Gaussian mirror. In terms of the Bayesian variable selection, the posterior is typically high-dimensional and analytically intractable. Exact inference methods based on sampling, such as Markov Chain Monte Carlo (MCMC), can encounter challenges related to mixing. Variational inference has emerged as an attractive alternative for approximating the posterior distribution. By recasting the sampling problem as an optimization problem, variational inference can significantly reduce computational time. In chapter 4, we apply the variational inference to group variable selection with spike-and-slab prior and propose an efficient parameter-expanded coordinate-ascent algorithm to obtain the optimal variational Bayes approximation. The proposed method has demonstrated good performance in both simulations and a real data example.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382777672Subjects--Topical Terms:

783654
Biostatistics.
Subjects--Index Terms:

Multiple Data SplittingIndex Terms--Genre/Form:

554714
Electronic books.

Problems in Variable Selection : = False Discovery Rate Control and Variational Inference.
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