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Implementing Bootstrap Resampling for Sequential Process Monitoring of Functional Data

Title: Implementing Bootstrap Resampling for Sequential Process Monitoring of Functional Data.
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Name(s): Archer, Andrew J., author
Type of Resource: text
Genre: Text
Bachelor Thesis
Date Issued: 2017-04-27
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: In many fields of statistical research, statisticians are challenged in producing risk limited, bias controlled data near instantaneously. For instance, in the area of signal detection, there exists a need to process mass quantities of data in a short time frame while simultaneously detecting the time at which a change in the process occurs. This has been a daunting task for many analytics professionals. Hawkins et al. (2003) outlined the traditional implementation of control charts for process monitoring. Charts including the cumulative sum and exponentially weighted moving average control chart were introduced as standard methods for identifying and monitoring process shifts. Unfortunately, these methods are limited by the need to estimate parameters, or are limited by simplifying parameters and known error distributions. To confront this challenge, we propose the use of a nonparametric method for process modeling of functional data that incorporates a variety of resampling methods.
Identifier: FSU_libsubv1_scholarship_submission_1493346509 (IID)
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1493346509
Owner Institution: FSU

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Archer, A. J. (2017). Implementing Bootstrap Resampling for Sequential Process Monitoring of Functional Data. Retrieved from http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1493346509