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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.
附註:	Advisor: Yu, Zhaoxia.
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:

182057
Statistics.
Subjects--Index Terms:

Statistical inference of change points

Statistical Inference of Change Points and Its Applications in Neuroscience Research.
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245 1 0 $a Statistical Inference of Change Points and Its Applications in Neuroscience Research.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
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 182057
650 4 $a Teaching. $3 179798
650 4 $a Research. $3 214540
650 4 $a Standard deviation. $3 915617
650 4 $a Simulation. $3 915618
650 4 $a Accuracy. $3 915619
650 4 $a Principal components analysis. $3 182575
650 4 $a Datasets. $3 915620
650 4 $a Convulsions & seizures. $3 915621
650 4 $a Epilepsy. $3 306381
650 4 $a Estimates. $3 915622
650 4 $a Dissertations & theses. $3 915623
650 4 $a Neurosciences. $3 211508
650 4 $a Noise. $3 670849
650 4 $a Methods. $3 915624
650 4 $a Algorithms. $3 184661
650 4 $a Electroencephalography. $3 194456
650 4 $a Time series. $3 915625
650 4 $a Confidence intervals. $3 183728
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 915616
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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