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Some Contributions to Statistical Si...

ProQuest Information and Learning Co.

Some Contributions to Statistical Signal Processing and Machine Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Some Contributions to Statistical Signal Processing and Machine Learning./
作者:	Gao, Qi.
面頁冊數:	1 online resource (85 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355150599

Some Contributions to Statistical Signal Processing and Machine Learning.
Gao, Qi.

Some Contributions to Statistical Signal Processing and Machine Learning. - 1 online resource (85 pages)

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

Thesis (Ph.D.)

Includes bibliographical references

With the rapid development of science and information technology, the amount of data has been growing at an unprecedented rate, which brings many interesting yet challenging problems for modern statistical research. We are faced with increasing dimensionality and complexity of data, which often requires new techniques to approach classical problems such as regression and model selection. In this dissertation, we discuss and propose solutions to three problems in the realm of statistical signal processing and machine learning with applications in high dimensional data. The first problem considers nonparametric modeling and break point detection for time series signal of counts using a genetic algorithm paired with radial basis expansion. We then study high dimensional variable selection in regression and classification with missing data while the missing fraction can be relatively large. Lastly, generalized fiducial inference for high-dimensional sparse additive models based on a spline representation is presented.

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

Mode of access: World Wide Web

ISBN: 9780355150599Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Some Contributions to Statistical Signal Processing and Machine Learning.
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099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Gao, Qi. $3 1182923
245 1 0 $a Some Contributions to Statistical Signal Processing and Machine Learning.
264 0 $c 2017
300 $a 1 online resource (85 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 Adviser: Thomas Lee.
502 $a Thesis (Ph.D.) $c University of California, Davis $d 2017.
504 $a Includes bibliographical references
520 $a With the rapid development of science and information technology, the amount of data has been growing at an unprecedented rate, which brings many interesting yet challenging problems for modern statistical research. We are faced with increasing dimensionality and complexity of data, which often requires new techniques to approach classical problems such as regression and model selection. In this dissertation, we discuss and propose solutions to three problems in the realm of statistical signal processing and machine learning with applications in high dimensional data. The first problem considers nonparametric modeling and break point detection for time series signal of counts using a genetic algorithm paired with radial basis expansion. We then study high dimensional variable selection in regression and classification with missing data while the missing fraction can be relatively large. Lastly, generalized fiducial inference for high-dimensional sparse additive models based on a spline representation is presented.
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 University of California, Davis. $b Statistics. $3 1182924
773 0 $t Dissertation Abstracts International $g 79-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10279007 $z click for full text (PQDT)

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