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    This study implements a Pearson chi-square statistic to test bivariate normality for latent variables in Type 1 censored models. Simulation studies and an empirical example demonstrate the test

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    Area of Science:

    • Statistics
    • Biostatistics
    • Econometrics

    Background:

    • Bivariate normality is a key assumption in many statistical models, particularly in survival analysis and econometrics.
    • Testing the null hypothesis of bivariate normality for latent variables is crucial for model validity.
    • Existing methods may lack robustness or applicability to censored data.

    Purpose of the Study:

    • To implement and evaluate a standard Pearson chi-square statistic for testing bivariate normality of latent variables in Type 1 censored models.
    • To address the need for a reliable statistical test in this specific modeling context.
    • To provide a practical tool for researchers working with censored data.

    Main Methods:

    • Implementation of a standard Pearson chi-square statistic.
    • Assessment of the statistic's behavior through extensive simulation studies.
    • Illustration of the test's application using an empirical example.

    Main Results:

    • The Pearson chi-square statistic demonstrates utility in testing the null hypothesis of bivariate normality for latent variables in Type 1 censored models.
    • Simulation studies provide insights into the statistic's performance characteristics.
    • The empirical example highlights practical application and interpretation.

    Conclusions:

    • The implemented Pearson chi-square statistic offers a viable method for assessing bivariate normality in Type 1 censored models.
    • The study underscores the importance of normality testing for latent variables in such models.
    • Limitations of the test are discussed, guiding future research and application.