• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics and computing >A closed-form filter for binary time series
【24h】

A closed-form filter for binary time series

机译：二进制时间序列的闭合滤波器

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

Non-Gaussian state-space models arise in several applications, and within this framework the binary time series setting provides a relevant example. However, unlike for Gaussian state-space models - where filtering, predictive and smoothing distributions are available in closed form - binary state-space models require approximations or sequential Monte Carlo strategies for inference and prediction. This is due to the apparent absence of conjugacy between the Gaussian states and the likelihood induced by the observation equation for the binary data. In this article we prove that the filtering, predictive and smoothing distributions in dynamic probit models with Gaussian state variables are, in fact, available and belong to a class of unified skew-normals (sun) whose parameters can be updated recursively in time via analytical expressions. Also the key functionals of these distributions are, in principle, available, but their calculation requires the evaluation of multivariate Gaussian cumulative distribution functions. Leveraging sun properties, we address this issue via novel Monte Carlo methods based on independent samples from the smoothing distribution, that can easily be adapted to the filtering and predictive case, thus improving state-of-the-art approximate and sequential Monte Carlo inference in small-to-moderate dimensional studies. Novel sequential Monte Carlo procedures that exploit the sun properties are also developed to deal with online inference in high dimensions. Performance gains over competitors are outlined in a financial application.
机译：在多个应用程序中出现非高斯状态空间模型，在此框架内，二进制时间序列设置提供了相关示例。然而，与高斯状态空间模型不同 - 在闭合形式的滤波，预测和平滑分布中可用的地方 - 二进制状态空间模型需要近似或连续的蒙特卡罗策略进行推理和预测。这是由于高斯状态之间的明显缺乏缀合物和由二进制数据的观察方程所致的可能性。在本文中，我们证明了具有高斯状态变量的动态探测模型中的过滤，预测和平滑分布，实际上是可用的，并且属于一类统一的偏斜正常（Sun），其参数可以通过分析及时递归地更新其参数表达。此外，这些分布的关键功能原则上可用，但它们的计算需要评估多元高斯累积分布函数。利用太阳属性，我们通过基于来自平滑分布的独立样品的新颖蒙特卡罗方法来解决这个问题，这很容易适应过滤和预测情况，从而改善最先进的近似和连续的蒙特卡罗推理小于中等的维度研究。还开发了利用太阳属性的新型汇流蒙特卡罗程序，以处理高维度的在线推理。在财务申请中概述了竞争对手的性能收益。

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