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Analysis Tools for Small and Big Dat...

Chen, Juan.

Analysis Tools for Small and Big Data Problems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Analysis Tools for Small and Big Data Problems./
Author:	Chen, Juan.
Description:	1 online resource (94 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355152388

Analysis Tools for Small and Big Data Problems.
Chen, Juan.

Analysis Tools for Small and Big Data Problems. - 1 online resource (94 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

The dissertation focuses on two separate problems. Each is informed by real-world applications. The first problem involves the assessment of an ordinal measurement system in a manufacturing setting. A random-effects model is proposed that is applicable to this repeatability and reproducibility context, and a Bayesian framework is adopted to facilitate inference. This first problem is an example of an analysis tool to solve a small data problem.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355152388Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Analysis Tools for Small and Big Data Problems.
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020 $a 9780355152388
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035 $a AAI10602920
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Chen, Juan. $3 1179479
245 1 0 $a Analysis Tools for Small and Big Data Problems.
264 0 $c 2017
300 $a 1 online resource (94 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Advisers: Kenneth J. Ryan; Mark V. Culp.
502 $a Thesis (Ph.D.) $c West Virginia University $d 2017.
504 $a Includes bibliographical references
520 $a The dissertation focuses on two separate problems. Each is informed by real-world applications. The first problem involves the assessment of an ordinal measurement system in a manufacturing setting. A random-effects model is proposed that is applicable to this repeatability and reproducibility context, and a Bayesian framework is adopted to facilitate inference. This first problem is an example of an analysis tool to solve a small data problem.
520 $a The second problem involves statistical machine learning applied to big data problems. As more and more data become available, a need increases to automate the ability to identify particularly relevant features in a prediction or forecasting context. This often involves expanding features using kernel functions to better facilitate predictive capabilities. Simultaneously, there are often manifolds embedded within big data structures that can be exploited to improve predictive performance on real data sets. Bringing together manifold learning with kernel methods provides a powerful and novel tool developed in this dissertation.
520 $a This dissertation has the advantage of contributing to a more-classical problem in statistics involving ordinal data and to cutting edge machine learning techniques for the analysis of big data. It is our contention that statisticians need to understand both problem types. The novel tools developed here are demonstrated on practical applications with strong results.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a West Virginia University. $b Eberly College of Arts & Sciences. $3 1179480
773 0 $t Dissertation Abstracts International $g 79-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10602920 $z click for full text (PQDT)

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