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Related Experiment Videos

A program for the analysis of incomplete two-way cross-classifications

M A Moussa

    Computer Programs in Biomedicine
    |June 1, 1980
    PubMed
    Summary

    This study introduces a program for smoothing incomplete contingency tables by adjusting zero cell counts. It helps in selecting the best data array for fitting log-linear models and assessing their goodness of fit.

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    Area of Science:

    • Statistics
    • Data Analysis

    Background:

    • Incomplete two-way contingency tables present challenges in statistical analysis.
    • Existing methods may struggle with zero cell counts and accurate model fitting.

    Purpose of the Study:

    • To develop a program for smoothing incomplete contingency tables.
    • To provide a method for selecting optimal smoothed data for log-linear model analysis.

    Main Methods:

    • The program adjusts zero cell counts using apriori probabilities (lambda ij) and a weighting factor (K).
    • It computes a risk function to select the best smoothed array.
    • Estimates parameters for hierarchical log-linear models and calculates likelihood ratios for goodness-of-fit testing.

    Main Results:

    • The program generates five arrays of smoothed cell counts.
    • It identifies the optimal array based on a computed risk function.
    • Provides parameter estimates and goodness-of-fit statistics for various log-linear models.

    Conclusions:

    • The developed program offers a robust approach to handling incomplete contingency tables.
    • It facilitates accurate parameter estimation and model evaluation in log-linear analysis.

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