重抽样方法
使用自助法、刀切法和交叉验证法对数据集重抽样
当不满足参数化检验假设或者来自非正态分布的样本较小时，可以使用重抽样方法来估计样本数据的描述性统计量和置信区间。自助方法以有放回方式从样本数据中选择随机样本，以估计所关注参数的置信区间。刀切法使用样本数据的子集系统地重新计算所关注的参数，每次在子集外留一个观测值（留一法重抽样）。根据这些计算，在整个数据样本中对所关注的参数进行估计。如果您拥有 Parallel Computing Toolbox™ 许可证，可以通过并行计算加快重抽样计算的速度。
函数
bootci | Bootstrap confidence interval |
bootstrp | Bootstrap sampling |
crossval | Estimate loss using cross-validation |
datasample | Randomly sample from data, with or without replacement |
jackknife | Jackknife sampling |
randsample | 随机样本 |
主题
- Resampling Statistics
Use bootstrap and jackknife methods to measure the uncertainty in the estimated parameters and statistics.
- Quick Start Parallel Computing for Statistics and Machine Learning Toolbox
Get started with parallel statistical computing.
- Implement Jackknife Using Parallel Computing
Speed up the jackknife using parallel computing.
- Implement Cross-Validation Using Parallel Computing
Speed up cross-validation using parallel computing.
- Implement Bootstrap Using Parallel Computing
Speed up the bootstrap using parallel computing.
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