Non- and semi-parametric techniques for handling missing data
Year: 2005
Type: dissertation
Abstract: Missing data arise in various settings, including surveys, clinical trials and epidemiological studies. With \nor without missing data, the goal of a statistical analysis is to make valid and efficient inferences about \na population of interest. The issue of missing values complicates this process. Early on, modelling \nincomplete data relied on the use of parametric models. Recently, there is a general trend towards non- \nand semi-parametric approaches to relax assumptions on which parametric models typically rely. Non- \nand semi-parametric procedures in general will not be as efficient as model-based techniques when there \nis a posited model, and the model is appropriate. However, if the assumed model is not the correct one, \ninferences can be worse than useless, leading to misleading interpretations of the data. \nIn this work, a variety of non- and semi-parametric techniques are used to handle missing data \nproblems. The material presented clearly shows the benefits of relaxing assumptions. \nWhile starting off with a basic introduction into the field of missing data and non- and semi-parametric \ntechniques, the successive parts of this work focus on different topics. A first part describes a kernel based \nimputation procedure which makes use of a non-parametric regression relationship between a partially \nobserved response and fully observed covariate. The approach is related to the approximate Bayesian \nbootstrap method and can be seen as an extension of the local single imputation of Cheng (1994) to a \nproper local multiple imputation approach. An essential ingredient of the algorithm is the local generation \nof responses....
Author: Hens, Niel
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2336827097