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The Pearson and likelihood ratio statistics are well-known in goodness-of-fit testing and are commonly used for models applied to multinomial count data. When data are from a table formed by

The Pearson and likelihood ratio statistics are well-known in goodness-of-fit testing and are commonly used for models applied to multinomial count data. When data are from a table formed by the cross-classification of a large number of variables, these goodness-of-fit statistics may have lower power and inaccurate Type I error rate due to sparseness. Pearson's statistic can be decomposed into orthogonal components associated with the marginal distributions of observed variables, and an omnibus fit statistic can be obtained as a sum of these components.

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    Date Created
    • 2018
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    • Partial requirement for: Ph.D., Arizona State University, 2018
      Note type
      thesis
    • Includes bibliographical references (pages 116-118)
      Note type
      bibliography
    • Field of study: Statistics

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    by Mudiyanselage Maduranga Kasun Dassanayake

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