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Statistical Inference of Change Points and Its Applications in Neuroscience Research.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Inference of Change Points and Its Applications in Neuroscience Research./
作者:	Shen, Tong.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	128 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28416146
ISBN:	9798522947255

Statistical Inference of Change Points and Its Applications in Neuroscience Research.
Shen, Tong.

Statistical Inference of Change Points and Its Applications in Neuroscience Research. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 128 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--University of California, Irvine, 2021.

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

Change point detection is a critical analysis in various scientific fields such as finance, medicine, and climatology. Despite the recent developments in methods and algorithms, it remains challenging in many problems. In this dissertation, we address and apply the detection of change points in two research problems. The first problem was motivated by identifying the epileptic seizure onset time using multi-channel EEG data and detecting abrupt changes in stocks that might characterize major events in the financial market. We propose a change point method using spectral principal component analysis on multivariate time series. By combining multiple time series and allowing for lead-lag relationships, our method achieves not only improved detectability but also more precise estimate of the locations of change points. In the second problem, the goal was to detect the exact time points at which a neuron fires using observed noisy calcium fluorescence recordings. We solve this problem by developing a time-varying ℓ0 penalized approach to jointly detect spikes using a dynamic change point detection algorithm and estimate firing rates using a Gaussian-boxcar smoother. Our simulated and real studies demonstrate that improved accuracy can be achieved by robustly integrating the evolving neural dynamics within and across recording sessions in a longitudinal setting.

ISBN: 9798522947255Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Statistical inference of change points

Statistical Inference of Change Points and Its Applications in Neuroscience Research.
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300 $a 128 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Yu, Zhaoxia.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Change point detection is a critical analysis in various scientific fields such as finance, medicine, and climatology. Despite the recent developments in methods and algorithms, it remains challenging in many problems. In this dissertation, we address and apply the detection of change points in two research problems. The first problem was motivated by identifying the epileptic seizure onset time using multi-channel EEG data and detecting abrupt changes in stocks that might characterize major events in the financial market. We propose a change point method using spectral principal component analysis on multivariate time series. By combining multiple time series and allowing for lead-lag relationships, our method achieves not only improved detectability but also more precise estimate of the locations of change points. In the second problem, the goal was to detect the exact time points at which a neuron fires using observed noisy calcium fluorescence recordings. We solve this problem by developing a time-varying ℓ0 penalized approach to jointly detect spikes using a dynamic change point detection algorithm and estimate firing rates using a Gaussian-boxcar smoother. Our simulated and real studies demonstrate that improved accuracy can be achieved by robustly integrating the evolving neural dynamics within and across recording sessions in a longitudinal setting.
590 $a School code: 0030.
650 4 $a Statistics. $3 517247
650 4 $a Teaching. $3 517098
650 4 $a Research. $3 531893
650 4 $a Standard deviation. $3 3560390
650 4 $a Simulation. $3 644748
650 4 $a Accuracy. $3 3559958
650 4 $a Principal components analysis. $3 565921
650 4 $a Datasets. $3 3541416
650 4 $a Convulsions & seizures. $3 3682489
650 4 $a Epilepsy. $3 743095
650 4 $a Estimates. $3 3561047
650 4 $a Dissertations & theses. $3 3560115
650 4 $a Neurosciences. $3 588700
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650 4 $a Algorithms. $3 536374
650 4 $a Electroencephalography. $3 543293
650 4 $a Time series. $3 3561811
650 4 $a Confidence intervals. $3 566017
653 $a Statistical inference of change points
690 $a 0463
690 $a 0317
710 2 $a University of California, Irvine. $b Statistics - Ph.D.. $3 3557861
773 0 $t Dissertations Abstracts International $g 83-02B.
790 $a 0030
791 $a Ph.D.
792 $a 2021
793 $a English
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