• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Markov Chain Monte Carlo stochastic approximation algorithms, Smoothing Spline ANOVA frailty models and applications.
【24h】

Markov Chain Monte Carlo stochastic approximation algorithms, Smoothing Spline ANOVA frailty models and applications.

机译：马尔可夫链蒙特卡洛随机逼近算法，平滑样条ANOVA脆弱模型和应用。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This thesis contains three parts: (I) potential problems in the implementation of the Markov Chain Monte Carlo Stochastic Approximation Algorithms (MCMCSAA), remedies and new adaptive algorithms; (II) Smoothing Spline ANOVA (SS ANOVA) frailty models; and (III) application of methods in the first two parts to investigate hormone generating mechanisms.;Part I consists of Chapters one and two. Chapter one introduces MCMCSAA and new hybrid algorithms. These new algorithms are proposed to improve the speed and stability of the existing ones. Chapter two compares the performance of various MCMCSAA using simulations. We implement the existing algorithms and the new hybrid algorithms with three different stopping criterions. We explore the essential factors affecting the speed and precision of the algorithms, and find that MCMCSAA can be sensitive to the choices of the initial values, MCMC sample size, step-size and the form of the I-matrix. There is no ultimate best algorithm for both precision and efficiency. In general, the proposed hybrid algorithms are stable and fast.;Part II consists of Chapters three and four. Chapter three introduces new SS ANOVA frailty models for recurrent events data. The general estimation methods utilizing penalized likelihood are proposed for the SS ANOVA frailty models. We adapt and modify the MCMCSAA for the new frailty models. Chapter four carries out two sets of simulations for the new frailty models. One set is based on the Weibull distribution and the other one is based on Gumbel distribution. The bootstrap confidence intervals are constructed as well. From simulations, we conclude that all MCMCSAA with suitable stopping criteria provide sensible estimates. And the new hybrid algorithm G4 achieves the same precision in about half of the CPU time comparing to existing algorithms.;Part III consists of Chapter five. It applies the new frailty models with hybrid MCMCSAA algorithm to investigate hormone secretion generating mechanism. We estimate the hazard function of inter-arrival times. For our particular ACTH and cortisol data, frailty (unobserved heterogeneity) does not show up. The effects of amplitude and decay rate do not seem significant.
机译：本论文包括三个部分：（一）马尔可夫链蒙特卡洛随机近似算法（MCMCSAA）的实现中存在的潜在问题，补救措施和新的自适应算法； （二）平滑样条方差分析（SS ANOVA）的脆弱模型； （三）前两部分方法的应用，研究激素的产生机理。第一部分为第一章和第二章。第一章介绍了MCMCSAA和新的混合算法。提出这些新算法以提高现有算法的速度和稳定性。第二章使用仿真比较了各种MCMCSAA的性能。我们使用三种不同的停止准则来实现现有算法和新混合算法。我们探索了影响算法速度和精度的基本因素，发现MCMCSAA对初始值，MCMC样本大小，步长和I矩阵形式的选择敏感。对于精度和效率都没有最终的最佳算法。总的来说，所提出的混合算法是稳定且快速的。第二部分包括第三章和第四章。第三章介绍了用于重复事件数据的新SS ANOVA脆弱模型。针对SS ANOVA脆弱模型，提出了利用惩罚似然性的一般估计方法。我们针对新的脆弱模型修改并修改了MCMCSAA。第四章对新的脆弱模型进行了两组仿真。一组基于威布尔分布，另一组基于Gumbel分布。引导置信区间也被构造。通过模拟，我们得出结论，所有具有适当停止标准的MCMCSAA都提供了合理的估计。与现有算法相比，新的混合算法G4在大约一半的CPU时间上达到了相同的精度。第三部分是第五章。将新的脆弱模型与混合MCMCSAA算法相结合，研究激素分泌的产生机理。我们估计到达间隔时间的危险函数。对于我们特定的ACTH和皮质醇数据，没有显示出脆弱性（未观察到的异质性）。幅度和衰减率的影响似乎并不明显。

著录项

  • 作者

    Jiang, Yihua.;

  • 作者单位

    University of California, Santa Barbara.;

  • 授予单位 University of California, Santa Barbara.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 117 p.
  • 总页数 117
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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