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Functional Data Analytics for Wearab...

Wrobel, Julia.

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Functional Data Analytics for Wearable Device and Neuroscience Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Functional Data Analytics for Wearable Device and Neuroscience Data./
作者:	Wrobel, Julia.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	126 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Contained By:	Dissertations Abstracts International81-02B.
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13902882
ISBN:	9781085591270

Functional Data Analytics for Wearable Device and Neuroscience Data.
Wrobel, Julia.

Functional Data Analytics for Wearable Device and Neuroscience Data. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 126 p.

Source: Dissertations Abstracts International, Volume: 81-02, Section: B.

Thesis (Ph.D.)--Columbia University, 2019.

This item must not be sold to any third party vendors.

This thesis uses methods from functional data analysis (FDA) to solve problems from three scientific areas of study. While the areas of application are quite distinct, the common thread of functional data analysis ties them together. The first chapter describes interactive open-source software for explaining and disseminating results of functional data analyses. Chapters two and three use curve alignment, or registration, to solve common problems in accelerometry and neuroimaging, respectively. The final chapter introduces a novel regression method for modeling functional outcomes that are trajectories over time. The first chapter of this thesis details a software package for interactively visualizing functional data analyses. The software is designed to work for a wide range of datasets and several types of analyses. This chapter describes that software and provides an overview ofFDA in different contexts. The second chapter introduces a framework for curve alignment, or registration, of exponential family functional data. The approach distinguishes itself from previous registration methods in its ability to handle dense binary observations with computational efficiency. Motivation comes from the Baltimore Longitudinal Study on Aging, in which accelerometer data provides valuable insights into the timing of sedentary behavior. The third chapter takes lessons learned about curve registration from the second chapter and use them to develop methods in an entirely new context: large multisite brain imaging studies. Scanner effects in multisite imaging studies are non-biological variability due to technical differences across sites and scanner hardware. This method identifies and removes scanner effects by registering cumulative distribution functions of image intensities values. In the final chapter the focus shifts from curve registration to regression. Described within this chapter is an entirely new nonlinear regression framework that draws from both functional data analysis and systems of ordinary equations. This model is motivated by the neurobiology of skilled movement, and was developed to capture the relationship between neural activity and arm movement in mice.

ISBN: 9781085591270Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Functional data analysis

Functional Data Analytics for Wearable Device and Neuroscience Data.
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