Asymptotic Robustness Study of the Polychoric Correlation Estimation

Shaobo Jin () and Fan Yang-Wallentin ()
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Shaobo Jin: Uppsala University
Fan Yang-Wallentin: Uppsala University

Psychometrika, 2017, vol. 82, issue 1, No 4, 67-85

Abstract: Abstract Asymptotic robustness against misspecification of the underlying distribution for the polychoric correlation estimation is studied. The asymptotic normality of the pseudo-maximum likelihood estimator is derived using the two-step estimation procedure. The t distribution assumption and the skew-normal distribution assumption are used as alternatives to the normal distribution assumption in a numerical study. The numerical results show that the underlying normal distribution can be substantially biased, even though skewness and kurtosis are not large. The skew-normal assumption generally produces a lower bias than the normal assumption. Thus, it is worth using a non-normal distributional assumption if the normal assumption is dubious.

Keywords: underlying distribution; asymptotic covariance matrix; non-normality; pseudo-maximum likelihood (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11336-016-9512-2

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