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Virginia Tech

1. Chu, Shuyu. Change Detection and Analysis of Data with Heterogeneous Structures.

Degree: PhD, Statistics, 2017, Virginia Tech

Heterogeneous data with different characteristics are ubiquitous in the modern digital world. For example, the observations collected from a process may change on its mean or variance. In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical modeling and analysis challenging. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas. Advisors/Committee Members: Marathe, Achla (committeechair), Deng, Xinwei (committeechair), Zhu, Hongxiao (committee member), Reynolds, Marion R (committee member).

Subjects/Keywords: Adaptive network lasso; Gaussian process; generalized likelihood ratio; logistic regression; mixed-type observation; particle filter; robustness; spectral mixture kernels; State space model; thermal image data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chu, S. (2017). Change Detection and Analysis of Data with Heterogeneous Structures. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78613

Chicago Manual of Style (16th Edition):

Chu, Shuyu. “Change Detection and Analysis of Data with Heterogeneous Structures.” 2017. Doctoral Dissertation, Virginia Tech. Accessed October 24, 2017. http://hdl.handle.net/10919/78613.

MLA Handbook (7th Edition):

Chu, Shuyu. “Change Detection and Analysis of Data with Heterogeneous Structures.” 2017. Web. 24 Oct 2017.

Vancouver:

Chu S. Change Detection and Analysis of Data with Heterogeneous Structures. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2017 Oct 24]. Available from: http://hdl.handle.net/10919/78613.

Council of Science Editors:

Chu S. Change Detection and Analysis of Data with Heterogeneous Structures. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/78613

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