| dc.contributor.author | Sneddon, William Gary. | en_US |
| dc.date.accessioned | 2014-10-21T12:38:08Z | |
| dc.date.available | 1998 | |
| dc.date.issued | 1998 | en_US |
| dc.identifier.other | AAINQ36562 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10222/55573 | |
| dc.description | In many physical processes linear models can arise from the discretization of a continuous process in order to describe behavior over an entire field. An important consequence is the models may have more parameters than observations. To alleviate this problem, one can impose a smoothness constraint on the parameters that reflects some prior knowledge of the physical process in order to obtain sensible estimates. | en_US |
| dc.description | A linear model is developed that has random explanatory and response variables, and a smoothness penalty is imposed based on the signal-to-noise ratio of the model. Results are presented assuming the value of the ratio is fixed, and when a procedure for estimating its value is used. The estimates perform well using a prediction based criterion in both situations. Robust estimation procedures for the model are also developed. | en_US |
| dc.description | The methods are applied to modelling temperature and salinity data in the California Current, with the goal of using shallow water observations to predict deep ocean readings. | en_US |
| dc.description | Thesis (Ph.D.)--Dalhousie University (Canada), 1998. | en_US |
| dc.language | eng | en_US |
| dc.publisher | Dalhousie University | en_US |
| dc.publisher | | en_US |
| dc.subject | Statistics. | en_US |
| dc.title | Smoothing techniques in underdetermined linear models. | en_US |
| dc.type | text | en_US |
| dc.contributor.degree | Ph.D. | en_US |
