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    • University of Missouri-Columbia
    • Graduate School - MU Theses and Dissertations (MU)
    • Theses and Dissertations (MU)
    • Dissertations (MU)
    • 2009 Dissertations (MU)
    • 2009 MU dissertations - Freely available online
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    Statistical analysis for survival data with missing information

    Zhang, Bin, 1979-
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    [PDF] short.pdf (18.17Kb)
    [PDF] research.pdf (452.0Kb)
    Date
    2009
    Format
    Thesis
    Metadata
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    Abstract
    As a branch of statistics, survival analysis, which is often referred to as "reliability theory" in engineering, has a long history. While in practical problems, some information might be missing. This dissertation discusses two types of missing information. The first type is caused by the censoring scheme, for example, the current status data. Several semiparametric models, e.g. the transformation models and the proportional odds models, are applied to the univariate and bivariate current status data. Efficient estimates are derived and the large sample properties of the estimates are provided. The other type is caused by sampling structure. It happens when the individuals do not have the same probabilities to be selected, for example, the stratified sampling. A biased sample problem is considered with the parameter of interest defined by some unbiased estimating equations. For the analysis, empirical likelihood approach is used and the likelihood ratio statistic is proved to follow a chi-square distribution asymptotically. Simulation studies and real data analysis are conducted to indicate that the presented approaches perform well in practical problems.
    URI
    https://hdl.handle.net/10355/9664
    https://doi.org/10.32469/10355/9664
    Degree
    Ph. D.
    Thesis Department
    Statistics (MU)
    Rights
    OpenAccess.
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
    Collections
    • 2009 MU dissertations - Freely available online
    • Statistics electronic theses and dissertations (MU)

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