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The estimating standard error of variance component for skewed distribution data in generalizability theory. (Chinese. English summary) Zbl 1289.62137

Summary: The aim of this paper is to explore how skew has effect on estimating standard error of variance component for generalizability theory. Using nature of generalized hyperbolic distribution, the study adopts Monte Carlo data simulation technique to simulate skewed distribution data. Traditional method, bootstrap method, jackknife method and Markov chain Monte Carlo (MCMC) method are used to compare estimating standard error of variance component for skewed distribution data in generalizability theory. Jackknife method is not good to estimate standard error of variance component for skewed distribution data. Traditional method and Markov chain Monte Carlo (MCMC) method are not very suitable, but can be accepted and bootstrap method is better. Skew of skewed distribution data has an effect on estimating standard error of variance component. Bootstrap method is a good adaptability to estimate standard error of variance component for generalizability theory. Skew has less effects on bootstrap method.

MSC:

62P15 Applications of statistics to psychology
65C05 Monte Carlo methods
62F40 Bootstrap, jackknife and other resampling methods
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