• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of computational and graphical statistics: A joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America >Bayesian Inference for the One-Factor Copula Model
【24h】

Bayesian Inference for the One-Factor Copula Model

机译：贝叶斯推断为单因素copula模型

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

We develop efficient Bayesian inference for the one-factor copula model with two significant contributions over existing methodologies. First, our approach leads to straightforward inference on dependence parameters and the latent factor; only inference on the former is available under frequentist alternatives. Second, we develop a reversible jump Markov chain Monte Carlo algorithm that averages over models constructed from different bivariate copula building blocks. Our approach accommodates any combination of discrete and continuous margins. Through extensive simulations, we compare the computational and Monte Carlo efficiency of alternative proposed sampling schemes. The preferred algorithm provides reliable inference on parameters, the latent factor, and model space. The potential of the methodology is highlighted in an empirical study of 10 binary measures of socio-economic deprivation collected for 11,463 East Timorese households. The importance of conducting inference on the latent factor is motivated by constructing a poverty index using estimates of the factor. Compared to a linear Gaussian factor model, our model average improves out-of-sample fit. The relationships between the poverty index and observed variables uncovered by our approach are diverse and allow for a richer and more precise understanding of the dependence between overall deprivation and individual measures of well-being.
机译：我们为单因素Copula模型开发了高效的贝叶斯推断，具有对现有方法的两个显着贡献。首先，我们的方法导致依赖参数和潜在因子的简单推断;在常见的替代方案下，前者的推断只能获得。其次，我们开发了一种可逆的跳跃马尔可夫链Monte Carlo算法，其平均由不同的双变型Copula构建块构成的模型。我们的方法适用于离散和连续边缘的任何组合。通过广泛的模拟，我们比较替代提出的采样方案的计算和蒙特卡罗效率。优选的算法在参数，潜在因子和模型空间上提供可靠的推理。该方法的潜力在11,463家东帝汶家庭收集的10个社会经济剥夺二元措施的实证研究中突出了。通过使用因子的估计构建贫困指数来实现对潜在因子进行推断的重要性。与线性高斯因子模型相比，我们的型号平均值可提高样品拟合。我们的方法发现的贫困指数与观察到的变量之间的关系是多样的，并允许更丰富，更精确地了解对整体剥夺与各个福祉措施之间的依赖关系。

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