國立虎尾科技大學 |

Trend and Variable-Phase Seasonality Estimation from Functional Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Trend and Variable-Phase Seasonality Estimation from Functional Data./
作者:	Tai, Liang-Hsuan.
面頁冊數:	1 online resource (104 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9781369864076

Trend and Variable-Phase Seasonality Estimation from Functional Data.
Tai, Liang-Hsuan.

Trend and Variable-Phase Seasonality Estimation from Functional Data. - 1 online resource (104 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--The Florida State University, 2017.

Includes bibliographical references

The problem of estimating trend and seasonality has been studied over several decades, although mostly using single time series setup. This dissertation studies the problem of estimating these components from a functional data point of view, i.e. multiple curves, in situations where seasonal effects exhibit arbitrary time warpings or phase variability across different observations. Rather than ignoring the phase variability, or using an off-the-shelf alignment method to remove phase, we take a model-based approach and seek Maximum Likelihood Estimators (MLEs) of the trend and the seasonal effects, while performing alignments over the seasonal effects at the same time. The MLEs of trend, seasonality, and phase are computed using a coordinate descent based optimization method. We use bootstrap replication for computing confidence bands and for testing hypothesis about the estimated components. We also utilize log-likelihood for selecting the trend subspace, and for comparisons with other candidate models. This framework is demonstrated using experiments involving synthetic data and three real data (Berkeley growth velocity, U.S. electricity price, and USD exchange fluctuation). Our framework is further applied to another biological problem, significance analysis of gene sets of time-course gene expression data and outperform the state-of-the-art method.

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

Mode of access: World Wide Web

ISBN: 9781369864076Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Trend and Variable-Phase Seasonality Estimation from Functional Data.
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Advisers: Kyle A. Gallivan; Anuj Srivastava.
502 $a Thesis (Ph.D.)--The Florida State University, 2017.
504 $a Includes bibliographical references
520 $a The problem of estimating trend and seasonality has been studied over several decades, although mostly using single time series setup. This dissertation studies the problem of estimating these components from a functional data point of view, i.e. multiple curves, in situations where seasonal effects exhibit arbitrary time warpings or phase variability across different observations. Rather than ignoring the phase variability, or using an off-the-shelf alignment method to remove phase, we take a model-based approach and seek Maximum Likelihood Estimators (MLEs) of the trend and the seasonal effects, while performing alignments over the seasonal effects at the same time. The MLEs of trend, seasonality, and phase are computed using a coordinate descent based optimization method. We use bootstrap replication for computing confidence bands and for testing hypothesis about the estimated components. We also utilize log-likelihood for selecting the trend subspace, and for comparisons with other candidate models. This framework is demonstrated using experiments involving synthetic data and three real data (Berkeley growth velocity, U.S. electricity price, and USD exchange fluctuation). Our framework is further applied to another biological problem, significance analysis of gene sets of time-course gene expression data and outperform the state-of-the-art method.
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
650 4 $a Mathematics. $3 527692
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a The Florida State University. $b Mathematics. $3 1180541
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10261999 $z click for full text (PQDT)