首页> 外文期刊>Electronic Journal of Statistics >A comparison theorem for data augmentation algorithms with applications
【24h】

A comparison theorem for data augmentation algorithms with applications

机译:数据增强算法与应用程序的比较定理

获取原文
       

摘要

The data augmentation (DA) algorithm is considered a useful Markov chain Monte Carlo algorithm that sometimes suffers from slow convergence. It is often possible to convert a DA algorithm into a sandwich algorithm that is computationally equivalent to the DA algorithm, but converges much faster. Theoretically, the reversible Markov chain that drives the sandwich algorithm is at least as good as the corresponding DA chain in terms of performance in the central limit theorem and in the operator norm sense. In this paper, we use the sandwich machinery to compare two DA algorithms. In particular, we provide conditions under which one DA chain can be represented as a sandwich version of the other. Our results are used to extend Hobert and Marchev’s (2008) results on the Haar PX-DA algorithm and to improve the collapsing theorem of Liu et al. (1994) and Liu (1994). We also illustrate our results using Brownlee’s (1965) stack loss data.
机译:数据增强(DA)算法被认为是有用的马尔可夫链蒙特卡洛算法,有时会遇到收敛缓慢的问题。通常可以将DA算法转换为三明治算法,该算法在计算上等效于DA算法,但收敛速度更快。从理论上讲,驱动三明治算法的可逆马尔可夫链在中央极限定理和算子范数意义上至少与相应的DA链一样好。在本文中,我们使用三明治机械来比较两种DA算法。特别是,我们提供了一个条件,在该条件下,一个DA链可以表示为另一个的三明治形式。我们的结果被用于扩展Habert PX-DA算法的Hobert和Marchev(2008)的结果,并改善Liu等人的崩溃定理。 (1994)和Liu(1994)。我们还使用Brownlee(1965)的堆栈损失数据说明了我们的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号