| RESAMPLING METHOD UNDER DEPENDENT MODELS |
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| 作者姓名： | Shi Xiquan |
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| 作者单位： | Department of Statistics and Operational Research Fudan University，Shanghai，200433 Ghina.，Department of Statistics and Operational Research，Fudan University，Shanghai，200433 Ghina. |
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| 基金项目： | Projects supported by National Natural Science Foundation of China |
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| 摘 要： | As well known,the jackknife and the bootstrap methods fail for the mean of thedependent observations.Recently,the moving blocks jackknife and bootstrap havebeen proposed in the case of the dependent observations.For the mean of the strictlystationary and m-dependent observations,it has been proved that the proposeddistribution and variance estimators are weakly consistent.This paper proves that thedistribution and variance estimators are strongly consistent for the mean(and theregular functions of mean)of the strictly stationary and m-dependent or(?)-mixingobservations.
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| 收稿时间： | 1989/10/14 0:00:00 |
RESAMPLING METHOD UNDER DEPENDENT MODELS |
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| Institution: | Department of Statistics and Operational Research, Fudan University, Shanghai, 200433, China. and Department of Statistics and Operational Research, Fudan University, Shanghai, 200433, China. |
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| Abstract: | As we known,the jackknife and the bootstrap methods fail for the mean of the dependent observations.Recently,the moving blocks jackknife and bootstrap have been proposed in the case of the dependent observations.for the mean of the strictly stationary and m-dependent observations,it has been proved that the proposed distribution and variance estimators are weakly consistent.This paper proves that the distribution and variance estimators are strongly consistent for the mean (and the regular functions of mean) of the strictly stationary and m-dependent or $\varphi$-mixing observations. |
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