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Estimation and Inference in Metabolomics with Nonignorable Missing Data

Zhao, Shangshu (2021) Estimation and Inference in Metabolomics with Nonignorable Missing Data. Master's Thesis, University of Pittsburgh. (Unpublished)

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Mass-Spectrometry(MS) is one of the most important methods used to characterize metabolomics data. However large-scale MS metabolomics data always faces the problem of data points unobserved or lost, whose magnitude could reach a level where it can’t be simply ignored. To account for the information hidden within missing values, we developed a methods to analyze metabolomics data with missing data based on MetabMiss, a newly developed rigorous method to model missing values. Our methodology shows an overall better performance on estimating both coefficients and variance and other criteria, which gives us advantages doing further statistical inference. For each criteria, we demonstrate our method on a simulation data set to compare it to other classical methods.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Zhao, Shangshushz121@pitt.edushz121
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMcKennan, Christopherchm195@pitt.educhm195
Committee MemberIyengar, Satishssi@pitt.edussi
Committee MemberZhang, Tingtingtiz67@pitt.edutiz67
Date: 3 May 2021
Date Type: Publication
Defense Date: 2 April 2021
Approval Date: 3 May 2021
Submission Date: 7 April 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 36
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: metabolomics, missing not at random (MNAR), PCA, WGCNA.
Date Deposited: 03 May 2021 15:48
Last Modified: 03 May 2021 15:48

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