On Grouped Observation Level Interaction and a Big Data Monte Carlo Sampling Algorithm

Big Data is transforming the way we live. From medical care to social networks, data is playing a central role in various applications. As the volume and dimensionality of datasets keeps growing, designing effective data analytics algorithms emerges as an important research topic in statistics. In this dissertation, I will summarize our research on two data analytics algorithms: a visual analytics algorithm named Grouped Observation Level Interaction with Multidimensional Scaling and a big data Monte Carlo sampling algorithm named Batched Permutation Sampler. These two algorithms are designed to enhance the capability of generating meaningful insights and utilizing massive datasets, respectively.

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

2015

Author dc:creator

• Hu, Xinran

Chair dc:contributor.committeechair

• Leman, Scotland C.

Committee members dc:contributor.committeemember

• North, Christopher L.
• Smith, Eric P.
• House, Leanna L.

• Visual Analytics
• Big Data Monte Carlo Sampling

Statement dc:rights

• In Copyright

Licence dc:rights.uri

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

Dc Identifier Other

vt_gsexam:4290

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/51224