Nonparametric regression analysis
| dc.contributor.advisor | Lin, Lizhen, Ph. D. | en |
| dc.contributor.committeeMember | Myers, Maggie | en |
| dc.creator | Malloy, Shuling Guo | en |
| dc.date.accessioned | 2015-11-16T18:41:16Z | en |
| dc.date.accessioned | 2018-01-22T22:29:09Z | |
| dc.date.available | 2015-11-16T18:41:16Z | en |
| dc.date.available | 2018-01-22T22:29:09Z | |
| dc.date.issued | 2015-05 | en |
| dc.date.submitted | May 2015 | en |
| dc.date.updated | 2015-11-16T18:41:16Z | en |
| dc.description | text | en |
| dc.description.abstract | Nonparametric regression uses nonparametric and flexible methods in analyzing complex data with unknown regression relationships by imposing minimum assumptions on the regression function. The theory and applications of nonparametric regression methods with an emphasis on kernel regression, smoothing spines and Gaussian process regression are reviewed in this report. Two datasets are analyzed to demonstrate and compare the three nonparametric regression models in R. | en |
| dc.description.department | Statistics | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier | doi:10.15781/T2RW5R | en |
| dc.identifier.uri | http://hdl.handle.net/2152/32497 | en |
| dc.language.iso | en | en |
| dc.subject | Nonparametric regression | en |
| dc.title | Nonparametric regression analysis | en |
| dc.type | Thesis | en |