...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computational statistics & data analysis >Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates
【24h】

Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates

机译：具有空间协变量的非均匀点过程的引导核强度估计

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

The bias-variance trade-off for inhomogeneous point processes with covariates is theoretically and empirically addressed. A consistent kernel estimator for the first-order intensity function based on covariates is constructed, which uses a convenient relationship between the intensity and the density of events location. The asymptotic bias and variance of the estimator are derived and hence the expression of its infeasible optimal bandwidth. Three data-driven bandwidth selectors are proposed to estimate the optimal bandwidth. One of them is based on a new smooth bootstrap proposal which is proved to be consistent under a Poisson assumption. The other two are a rule-of-thumb method based on assuming normality, and a simple non-model-based approach. An extensive simulation study is accomplished considering Poisson and non-Poisson scenarios, and including a comparison with other competitors. The practicality of the new proposals is shown through an application to real data about wildfires in Canada, using meteorological covariates. (C) 2019 Elsevier B.V. All rights reserved.
机译：理论上和经验地解决了具有协变量的非均匀点过程的偏差差异折衷。构造了基于协变量的一致性强度函数的一致内核估计，其使用强度与事件位置的密度之间的方便关系。衍生估计器的渐近偏差和差异，从而产生其不可行的最佳带宽的表达。提出了三个数据驱动带宽选择器来估计最佳带宽。其中一个是基于新的平滑训练建议，该提案被证明在泊松假设下保持一致。另外两个是基于假设正常性的拇指方法，以及一种简单的非模型的方法。考虑泊松和非泊松情景，包括与其他竞争对手的比较实现了广泛的仿真研究。使用气象协变者，通过应用于加拿大的野火的实际数据来显示新提案的实用性。 （c）2019年Elsevier B.V.保留所有权利。

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