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Bootstrap inference of the skew-normal two-way classification random effects model with interaction
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作者 YE Ren-dao AN Na +1 位作者 LUO Kun LIN Ya 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2022年第3期435-452,共18页
In this paper,we consider the statistical inference problems for the fixed effect and variance component functions in the two-way classification random effects model with skewnormal errors.Firstly,the exact test stati... In this paper,we consider the statistical inference problems for the fixed effect and variance component functions in the two-way classification random effects model with skewnormal errors.Firstly,the exact test statistic for the fixed effect is constructed.Secondly,using the Bootstrap approach and generalized approach,the one-sided hypothesis testing and interval estimation problems for the single variance component,the sum and ratio of variance components are discussed respectively.Further,the Monte Carlo simulation results indicate that the exact test statistic performs well in the one-sided hypothesis testing problem for the fixed effect.And the Bootstrap approach is better than the generalized approach in the one-sided hypothesis testing problems for variance component functions in most cases.Finally,the above approaches are applied to the real data examples of the consumer price index and value-added index of three industries to verify their rationality and effectiveness. 展开更多
关键词 skew-normal two-way classification random effects model with interaction fixed effect variance component functions BOOTSTRAP generalized approach
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