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http://hdl.handle.net/1942/17548
Title: | Doubly-robust weight smoothing models to smooth post-stratification weights in case of a Gaussian survey outcome | Authors: | Reyes Sierra, Adriana Rocio | Advisors: | VANDENDIJCK, Yannick FAES, Christel |
Issue Date: | 2014 | Publisher: | tUL | 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. | Notes: | Master of Statistics-Biostatistics | Document URI: | http://hdl.handle.net/1942/17548 | Category: | T2 | Type: | Theses and Dissertations |
Appears in Collections: | Master theses |
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