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Generation and Analysis of Synthetic Data for Privacy Protection Under the Multivariate Linear Regression Model
Başlık:
Generation and Analysis of Synthetic Data for Privacy Protection Under the Multivariate Linear Regression Model
Yazar:
Zylstra, John, author.
ISBN:
9780355990102
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 electronic resource (152 pages)
Genel Not:
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Advisors: Bimal Sinha; Martin Klein Committee members: Yaakov Malinovsky; Thomas Mathew; Junyong Park.
Özet:
In this dissertation, the author derives likelihood-based exact inference for multiply imputed synthetic data under the multiple (p>1) univariate linear regression model and for singly and multiply imputed data under the multivariate linear regression model. In the former, the synthetic data are generated under plug-in sampling, where unknown parameters in the model are set equal to observed values of point estimators. In the latter, synthetic data are also generated under posterior predictive sampling where they are drawn from a posterior predictive distribution. Simulations are presented to confirm the methodology performs as the theory predicts and to evaluate privacy protection. Robustness studies are also given. In the final chapter, a new privacy protection method similar to bottom- and top-coding is proposed and its inferential properties explored.
Notlar:
School code: 0434
Konu Başlığı:
Tüzel Kişi Ek Girişi:
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Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(680821.1) | 680821-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
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