From Nonparametric Density Estimation to Parametric Estimation of Multidimensional Diffusion Processes  

作　　者：Julien Apala N’drin Ouagnina Hili 

机构地区：[1]Laboratory of Applied Mathematics and Computer Science, University Felix Houphouë t Boigny, Abidjan, Cô te d’Ivoire [2]Laboratory of Mathematics and New Technologies of Information, National Polytechnique Institute Houphouë t-Boigny of Yamoussoukro, Yamoussoukro, Cô te d’Ivoire

出　　处：《Applied Mathematics》2015年第9期1592-1610,共19页应用数学（英文）

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.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.

关 键 词：Hellinger Distance Estimation MULTIDIMENSIONAL Diffusion Processes STRONG MIXING Process CONSISTENCE ASYMPTOTIC NORMALITY 

分 类 号：O1[理学—数学]