The method of permutation is introduced for the block re, sampling of moving block bootstrap, and is compard with the fixed block bootstrap and stationary bootstrap. Simulations are proformed, which verify that the bl...The method of permutation is introduced for the block re, sampling of moving block bootstrap, and is compard with the fixed block bootstrap and stationary bootstrap. Simulations are proformed, which verify that the block resampling of moving block bootstrap with permutation can effectively eliminete the number of abnormal data in the resampled data set and reduce the resampling errors of estimation.展开更多
由于传统求解时间序列自回归(auto-regressive,AR)模型的最小二乘方法无法顾及设计矩阵误差,现有的AR模型迭代解法难以应用协方差传播率给出较为精确的精度评定公式。将块自助采样方法引入到AR模型精度评定研究中,并在其基础上借助Siev...由于传统求解时间序列自回归(auto-regressive,AR)模型的最小二乘方法无法顾及设计矩阵误差,现有的AR模型迭代解法难以应用协方差传播率给出较为精确的精度评定公式。将块自助采样方法引入到AR模型精度评定研究中,并在其基础上借助Sieve自助法的思想,定义了顾及设计矩阵误差AR模型精度评定的Sieve块自助采样方法。根据不同的分块准则和采样策略,给出了4种方法的重采样步骤。模拟实验结果表明,精度评定的Sieve块自助采样方法能够得到比最小二乘法、经典的AR模型迭代解法更加可靠的自回归系数标准差,具有更强的适用性。同时,北斗卫星精密钟差真实案例表明,所提出的Sieve移动块自助法、Sieve非重叠块自助法、Sieve圆形块自助法以及Sieve静止块自助法的均方根(root mean square,RMS)比总体最小二乘的RMS分别减小了70.25%、78.65%、70.89%和79.24%,进一步验证了所提算法的有效性和可靠性,为时间序列AR模型的精度评定问题提供了一种采样思路。展开更多
The bootstrap method is one of the new ways of studying statistical math which this article uses but is a major tool for studying and evaluating the values of parameters in probability distribution.Our research is con...The bootstrap method is one of the new ways of studying statistical math which this article uses but is a major tool for studying and evaluating the values of parameters in probability distribution.Our research is concerned overview of the theory of infinite distribution functions.The tool to deal with the problems raised in the paper is the mathematical methods of random analysis(theory of random process and multivariate statistics).In this article,we introduce the new function to find out the bias and standard error with jackknife method for Generalized Extreme Value distributions.展开更多
This paper introduced a bootstrap method called truncated geometric bootstrap method for time series stationary process. We estimate the parameters of a geometric distribution which has been truncated as a probability...This paper introduced a bootstrap method called truncated geometric bootstrap method for time series stationary process. We estimate the parameters of a geometric distribution which has been truncated as a probability model for the bootstrap algorithm. This probability model was used in resampling blocks of random length, where the length of each blocks has a truncated geometric distribution. The method was able to determine the block sizes b and probability p attached to its random selections. The mean and variance were estimated for the truncated geometric distribution and the bootstrap algorithm developed based on the proposed probability model.展开更多
文摘The method of permutation is introduced for the block re, sampling of moving block bootstrap, and is compard with the fixed block bootstrap and stationary bootstrap. Simulations are proformed, which verify that the block resampling of moving block bootstrap with permutation can effectively eliminete the number of abnormal data in the resampled data set and reduce the resampling errors of estimation.
文摘由于传统求解时间序列自回归(auto-regressive,AR)模型的最小二乘方法无法顾及设计矩阵误差,现有的AR模型迭代解法难以应用协方差传播率给出较为精确的精度评定公式。将块自助采样方法引入到AR模型精度评定研究中,并在其基础上借助Sieve自助法的思想,定义了顾及设计矩阵误差AR模型精度评定的Sieve块自助采样方法。根据不同的分块准则和采样策略,给出了4种方法的重采样步骤。模拟实验结果表明,精度评定的Sieve块自助采样方法能够得到比最小二乘法、经典的AR模型迭代解法更加可靠的自回归系数标准差,具有更强的适用性。同时,北斗卫星精密钟差真实案例表明,所提出的Sieve移动块自助法、Sieve非重叠块自助法、Sieve圆形块自助法以及Sieve静止块自助法的均方根(root mean square,RMS)比总体最小二乘的RMS分别减小了70.25%、78.65%、70.89%和79.24%,进一步验证了所提算法的有效性和可靠性,为时间序列AR模型的精度评定问题提供了一种采样思路。
文摘The bootstrap method is one of the new ways of studying statistical math which this article uses but is a major tool for studying and evaluating the values of parameters in probability distribution.Our research is concerned overview of the theory of infinite distribution functions.The tool to deal with the problems raised in the paper is the mathematical methods of random analysis(theory of random process and multivariate statistics).In this article,we introduce the new function to find out the bias and standard error with jackknife method for Generalized Extreme Value distributions.
文摘This paper introduced a bootstrap method called truncated geometric bootstrap method for time series stationary process. We estimate the parameters of a geometric distribution which has been truncated as a probability model for the bootstrap algorithm. This probability model was used in resampling blocks of random length, where the length of each blocks has a truncated geometric distribution. The method was able to determine the block sizes b and probability p attached to its random selections. The mean and variance were estimated for the truncated geometric distribution and the bootstrap algorithm developed based on the proposed probability model.
