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Gaussian Processes in Semi-Parametric Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Gaussian Processes in Semi-Parametric Models./
Author:	Thompson, Marten Lee.
Description:	1 online resource (215 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798379952327

Gaussian Processes in Semi-Parametric Models.
Thompson, Marten Lee.

Gaussian Processes in Semi-Parametric Models. - 1 online resource (215 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--University of Minnesota, 2023.

Includes bibliographical references

Gaussian processes provide a flexible, non-parametric prior for function estimation. We investigate the applicability of Gaussian processes in semi-parametric models to relax otherwise restrictive assumptions. Our first application of this perspective is climate time series, where we see both the advantage of Gaussian processes in semi-parametric models as well as their computational restrictions. Next, we use Gaussian processes to relax the assumed error distribution of traditional small area models. Finally, we turn our attention to stripping away assumptions on Gaussian processes themselves: can data be used to inform their parameterization? We detail our work on each of these problems and provide software for future researchers.

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

Mode of access: World Wide Web

ISBN: 9798379952327Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

BayesianIndex Terms--Genre/Form:

554714
Electronic books.

Gaussian Processes in Semi-Parametric Models.
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245 1 0 $a Gaussian Processes in Semi-Parametric Models.
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300 $a 1 online resource (215 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
500 $a Advisor: Chatterjee, Snigdhansu.
502 $a Thesis (Ph.D.)--University of Minnesota, 2023.
504 $a Includes bibliographical references
520 $a Gaussian processes provide a flexible, non-parametric prior for function estimation. We investigate the applicability of Gaussian processes in semi-parametric models to relax otherwise restrictive assumptions. Our first application of this perspective is climate time series, where we see both the advantage of Gaussian processes in semi-parametric models as well as their computational restrictions. Next, we use Gaussian processes to relax the assumed error distribution of traditional small area models. Finally, we turn our attention to stripping away assumptions on Gaussian processes themselves: can data be used to inform their parameterization? We detail our work on each of these problems and provide software for future researchers.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Applied mathematics. $3 1069907
653 $a Bayesian
653 $a Gaussian process
653 $a Semi-parametric
653 $a Small area statistics
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690 $a 0364
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Minnesota. $b Statistics. $3 1189322
773 0 $t Dissertations Abstracts International $g 85-01B.
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