منابع مشابه
Bootstrapping Principal Component Regression Models
Bootstrap methods can be used as an alternative for cross-validation in regression procedures such as principal component regression (PCR). Several bootstrap methods for the estimation of prediction errors and confidence intervals are presented. It is shown that bootstrap error estimates are consistent with cross-validation estimates but exhibit less variability. This makes it easier to select ...
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Spatial statistical models have been used extensively in many geospatial and environmental studies over several decades. While being very important, the issues of testing and validation in spatial statistical models are rarely investigated carefully in spatial environmental studies. Often strict theoretical asymptotic assumptions used in those models are left unexplored or unanswered in many st...
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In regression models, appropriate bootstrap methods for inference robust to heteroskedasticity of unknown form are the wild bootstrap and the pairs bootstrap. The finite sample performance of a heteroskedastic-robust test is investigated with Monte Carlo experiments. The simulation results suggest that one specific version of the wild bootstrap outperforms the other versions of the wild bootstr...
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Semiparametric estimation of the memory parameter in economic time series raises the problem of the small sample size and the poor approximation of the asymptotic distribution to the finite sample counterpart. This paper considers the bootstrap to improve the finite sample distribution of the popular log peridogram regression and shows that it can significantly reduce the error in the coverage ...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1981
ISSN: 0090-5364
DOI: 10.1214/aos/1176345638