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http://hdl.handle.net/1942/17548
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DC Field | Value | Language |
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dc.contributor.advisor | VANDENDIJCK, Yannick | - |
dc.contributor.advisor | FAES, Christel | - |
dc.contributor.author | Reyes Sierra, Adriana Rocio | - |
dc.date.accessioned | 2014-10-09T09:14:14Z | - |
dc.date.available | 2014-10-09T09:14:14Z | - |
dc.date.issued | 2014 | - |
dc.identifier.uri | http://hdl.handle.net/1942/17548 | - |
dc.description.abstract | In order to obtain unbiased estimates of a population quantity based on sample survey data, post-stratification techniques use external data to adjust the estimates during the analysis stage. Small sample sizes in any post- strata may yield highly variable estimator. The weight trimming method pools highly underrepresented units into a stratum with better representation but it is somehow arbitrary. In the same spirit, weight-smoothing approach treats post-stratum means as random-effects, inducing shrinkage across post-stratum means. To protect against the bias generated by possible misspecification of the mixed-model, a doubly-robust version is used as well as a nonparametric spline function for the underlying weight stratum means. I compare those approaches in a simulation study for the inference about the population mean of a normally distributed survey outcome with ordinal post-stratifying variable. None of the 9 estimators is uniformly best in all 24 scenarios considered but the nonparametric weight-smoothing doubly-robust is close to the best for a wide range of populations offering protection against unfavorable mean structures and model misspecification, therefore can be seen as a robust technique. The methods are illustrated by estimating the weekly working hours using data from the 2008 Quality of Life Survey in Colombia. | - |
dc.format.mimetype | Application/pdf | - |
dc.language | en | - |
dc.language.iso | en | - |
dc.publisher | tUL | - |
dc.title | Doubly-robust weight smoothing models to smooth post-stratification weights in case of a Gaussian survey outcome | - |
dc.type | Theses and Dissertations | - |
local.format.pages | 0 | - |
local.bibliographicCitation.jcat | T2 | - |
dc.description.notes | Master of Statistics-Biostatistics | - |
local.type.specified | Master thesis | - |
item.accessRights | Open Access | - |
item.contributor | Reyes Sierra, Adriana Rocio | - |
item.fullcitation | Reyes Sierra, Adriana Rocio (2014) Doubly-robust weight smoothing models to smooth post-stratification weights in case of a Gaussian survey outcome. | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Master theses |
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File | Description | Size | Format | |
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12334912013005.pdf | 652.98 kB | Adobe PDF | View/Open |
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