Withdraw
Loading…
Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables
Eisinger, Robert David
Content Files

Loading…
Download Files
Loading…
Download Counts (All Files)
Loading…
Edit File
Loading…
Permalink
https://hdl.handle.net/2142/92928
Description
- Title
- Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables
- Author(s)
- Eisinger, Robert David
- Issue Date
- 2016-07-08
- Director of Research (if dissertation) or Advisor (if thesis)
- Chen, Yuguo
- Doctoral Committee Chair(s)
- Chen, Yuguo
- Committee Member(s)
- Culpepper, Steven A.
- Marden, John I.
- Simpson, Douglas G.
- Department of Study
- Statistics
- Discipline
- Statistics
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- Ph.D.
- Degree Level
- Dissertation
- Date of Ingest
- 2016-11-10T18:27:42Z
- Keyword(s)
- Monte Carlo method
- Sequential importance sampling
- Counting problem
- Contingency Table
- Abstract
- We propose new sequential importance sampling methods for sampling contingency tables with fixed margins, loopless, undirected multigraphs, and high-dimensional tables. In each case, the proposals for the method are constructed by leveraging approximations to the total number of structures (tables, multigraphs, or high-dimensional tables), based on results in the literature. The methods generate structures that are very close to the target uniform distribution. Along with their importance weights, the data structures are used to approximate the null distribution of test statistics. In the case of contingency tables, we apply the methods to a number of applications and demonstrate an improvement over competing methods. For loopless, undirected multigraphs, we apply the method to ecological and security problems, and demonstrate excellent performance. In the case of high-dimensional tables, we apply the sequential importance sampling method to the analysis of multimarker linkage disequilibrium data and also demonstrate excellent performance.
- Graduation Semester
- 2016-08
- Type of Resource
- text
- Permalink
- http://hdl.handle.net/2142/92928
- Copyright and License Information
- Copyright 2016 Robert Eisinger
Owning Collections
Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisManage Files
Loading…
Edit Collection Membership
Loading…
Edit Metadata
Loading…
Edit Properties
Loading…
Embargoes
Loading…