A parametric bootstrap approach for one-way classification model with skew-normal random effects
| Autor: | Kun Luo, Ren-dao Ye, Ling Jiang, Li-jun Xu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Applied Mathematics
05 social sciences Skew Probability density function Moment-generating function Random effects model 01 natural sciences 010104 statistics & probability Distribution (mathematics) 0502 economics and business Expectation–maximization algorithm Applied mathematics 0101 mathematics 050205 econometrics Mathematics Parametric statistics Type I and type II errors |
| Zdroj: | Applied Mathematics-A Journal of Chinese Universities. 34:423-435 |
| ISSN: | 1993-0445 1005-1031 |
| DOI: | 10.1007/s11766-019-3564-x |
| Popis: | In this paper, several properties of one-way classification model with skew-normal random effects are obtained, such as moment generating function, density function and noncentral skew chi-square distribution, etc. Based on the EM algorithm, we discuss the maximum likelihood (ML) estimation of unknown parameters. For testing problem of fixed effect, a parametric bootstrap (PB) approach is developed. Finally, some simulation results on the Type I error rates and powers of the PB approach are obtained, which show that the PB approach provides satisfactory performances on the Type I error rates and powers, even for small samples. For illustration, our main results are applied to a real data problem. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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