基本信息来源于合作网站,原文需代理用户跳转至来源网站获取       
摘要:
The paper deals with the estimation of parameters of multidimensional diffusion processes that are discretely observed. We construct estimator of the parameters based on the minimum Hellinger distance method. This method is based on the minimization of the Hellinger distance between the density of the invariant distribution of the diffusion process and a nonparametric estimator of this density. We give conditions which ensure the existence of an invariant measure that admits density with respect to the Lebesgue measure and the strong mixing property with exponential rate for the Markov process. Under this condition, we define an estimator of the density based on kernel function and study his properties (almost sure convergence and asymptotic normality). After, using the estimator of the density, we construct the minimum Hellinger distance estimator of the parameters of the diffusion process and establish the almost sure convergence and the asymptotic normality of this estimator. To illustrate the properties of the estimator of the parameters, we apply the method to two examples of multidimensional diffusion processes.
推荐文章
内容分析
关键词云
关键词热度
相关文献总数  
(/次)
(/年)
文献信息
篇名 From Nonparametric Density Estimation to Parametric Estimation of Multidimensional Diffusion Processes
来源期刊 应用数学(英文) 学科 数学
关键词 Hellinger DISTANCE Estimation MULTIDIMENSIONAL Diffusion Processes Strong MIXING Process CONSISTENCE ASYMPTOTIC NORMALITY
年,卷(期) yysxyw_2015,(9) 所属期刊栏目
研究方向 页码范围 1592-1610
页数 19页 分类号 O1
字数 语种
DOI
五维指标
传播情况
(/次)
(/年)
引文网络
引文网络
二级参考文献  (0)
共引文献  (0)
参考文献  (0)
节点文献
引证文献  (0)
同被引文献  (0)
二级引证文献  (0)
2015(0)
  • 参考文献(0)
  • 二级参考文献(0)
  • 引证文献(0)
  • 二级引证文献(0)
研究主题发展历程
节点文献
Hellinger
DISTANCE
Estimation
MULTIDIMENSIONAL
Diffusion
Processes
Strong
MIXING
Process
CONSISTENCE
ASYMPTOTIC
NORMALITY
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
应用数学(英文)
月刊
2152-7385
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
1878
总下载数(次)
0
论文1v1指导