Small area estimation via generalized linear models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand
| dc.contributor.author | Noble, Alasdair D. L. | |
| dc.date.accessioned | 2010-12-17T00:44:14Z | |
| dc.date.available | NO_RESTRICTION | en_US |
| dc.date.available | 2010-12-17T00:44:14Z | |
| dc.date.issued | 2003 | |
| dc.description.abstract | Survey information is commonly collected to yield estimates of quantities for large geographic areas, for example, complete countries. However the estimates of those quantities at much smaller geographic areas are often of interest and the sample sizes in these areas are generally too small to give useful results. Small area estimation is used to make inference about those small areas with greater precision than the direct estimates, either by exploiting similarities between different small areas or by accessing additional information often from administrative records. The majority of the traditional small area estimation methods are examples of a simple linear model Marker (1999) and this work begins by extending the model to a generalized linear model (GLM) Nelder and Wedderburn (1972) and then including structure preserving estimation (SPREE) in the classification. This had not been done previously. SPREE had previously been fitted using the iterative proportional fitting algorithm Deming and Stephan (1940) which could be described as a "black box" approach. By expressing SPREE in terms of a GLM an alternative algorithm for fitting the method is developed which elucidates the underlying concepts. This new approach allows the method to be extended from the contingency table with categorical variables which the IPF could fit, to continuous variables and random effects models. An example including a continuous variable is given. SPREE is a method which uses auxiliary information as well as survey data. In the past assumptions about appropriate auxiliary information have been made with little theoretical support. The new approach allows these assumptions to be considered and they are found to be wanting in some cases. An example based on a national survey in New Zealand for unemployment statistics, is used extensively throughout the thesis. These data have characteristics that make analysis in the Bayesian paradigm appropriate. This paradigm has been applied and a conditional autoregressive error structure is considered. Finally relative risk models are considered. It is shown that these could have been fitted using the IPF algorithm but the new approach allows combinations of other modeling techniques which are not available using IPF. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10179/2011 | |
| dc.language.iso | en | en_US |
| dc.publisher | Massey University | en_US |
| dc.rights | The Author | en_US |
| dc.subject | Statistical sampling | en_US |
| dc.subject | Sampling (Statistics) | |
| dc.subject.other | Fields of Research::230000 Mathematical Sciences::230200 Statistics::230204 Applied statistics | en_US |
| dc.title | Small area estimation via generalized linear models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand | en_US |
| dc.type | Thesis | en_US |
| massey.contributor.author | Noble, Alasdair D. L. | |
| thesis.degree.discipline | Statistics | en_US |
| thesis.degree.grantor | Massey University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) | en_US |
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