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    Robust data analysis based on characteristic functions

    Author
    Schumaker, Arlyn D.
    View/Open
    178099_thesis.pdf (3.692Mb)
    Other Contributors
    Paulson, A. S.; Manners, George E., 1943-; Sullo, Pasquale; Wilkinson, John W.;
    Date Issued
    1976-05
    Subject
    Operations research and statistics
    Degree
    PhD;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
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    URI
    https://hdl.handle.net/20.500.13015/1918
    Abstract
    Characteristic functions have heretofore mainly been a theoretical tool in the theory of probability and mathematical statistics and have been all but neglected in the field of data analysis. The research presented in this thesis indicates the usefulness of the characteristic function in the area of data analysis, specifically robust estimation. By investigating the minimization over the distributional parameters of the integral of the weighted squared modulus of the difference between the theoretical and empirical characteristic function, a relatively simple fixed point procedure was obtained for the estimation of the parameters of univariate sample data using the underlying assumption of normality. Sensitivity and influence curves and breakdown analysis indicate the robustness qualities of the estimates. The procedures are extended to the multivariate normal parameter estimation problem and are shown to obtain robust estimates of location, scale and correlation. Finally, the univariate procedure is applied to the general linear regression problem by minimizing the scale estimate of the residual under the hypothesized model. Diagnostics are provided to indicate the degree of an observation's compatibility with the underlying assumptions and the estimated parameters. Analyses of various examples are presented which indicate the use of the procedures and diagnostics in a data analytic framework.;
    Description
    May 1976; School of Management
    Department
    Dept. of Management and Technology;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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    • RPI Theses Online (Complete)

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