Change Detection and Analysis of Data with Heterogeneous Structures

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.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Statistics

Department dc:contributor.department

Statistics

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2017

Author dc:creator

• Chu, Shuyu

Chairs dc:contributor.committeechair

• Marathe, Achla
• Deng, Xinwei

Committee members dc:contributor.committeemember

• Zhu, Hongxiao
• Reynolds, Marion R. Jr.

• 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

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:11656

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/78613