Results 31 to 40 of about 6,198,544 (193)

Diffusions with measurement errors. I. Local Asymptotic Normality [PDF]

ESAIM: Probability and Statistics, 2001
Summary: We consider a diffusion process \(X\) which is observed at times \(i/n\) for \(i=0,1,\dots,n\), each observation being subject to a measurement error. All errors are independent and centered Gaussian with known variance \(\rho_n\). There is an unknown parameter within the diffusion coefficient, to be estimated.
Gloter, Arnaud, Jacod, Jean
openaire   +1 more source

The Local Linear M-Estimation with Missing Response Data

Journal of Applied Mathematics, 2014
This paper studies the nonparametric regressive function with missing response data. Three local linear M-estimators with the robustness of local linear regression smoothers are presented such that they have the same asymptotic normality and consistency.
Shuanghua Luo, Cheng-Yi Zhang, Fengmin Xu   +2 more
doaj   +1 more source

Asymptotic estimation for statistical models of continuous-time discrete martingales

Lietuvos Matematikos Rinkinys
The paper deals with statistical experiments of the continuous-time discrete local martingales, including models of all types of point processes. The process of local density of the discrete local martingales is expressed by a stochastic exponent of the
Vaidotas Kanišauskas, Karolina Kanišauskienė   +1 more
doaj   +3 more sources

Variable bandwidth local maximum likelihood type estimation for diffusion processes

Advances in Difference Equations, 2018
The method of robust approach is applied to estimate drift function and diffusion function of diffusion processes with discrete-time observations. The proposed method combines the ideas of local linear regression technique and maximum likelihood type ...
Ming T. Tang, Yun Y. Wang
doaj   +1 more source

Expectile Regression on Distributed Large-Scale Data

IEEE Access, 2020
Large-scale data presents great challenges to data analysis due to the limited computer storage capacity and the heterogeneous data structure. In this article, we propose a distributed expectile regression model to resolve the challenges of large-scale ...
Aijun Hu, Chujin Li, Jing Wu
doaj   +1 more source

Local asymptotic normality in of standard generalized Pareto processes [PDF]

Journal of Statistical Planning and Inference, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Aulbach, Stefan, Falk, Michael
openaire   +1 more source

Maximum Likelihood Estimators for a Supercritical Branching Diffusion Process

International Journal of Mathematics and Mathematical Sciences, 2012
The log-likelihood of a nonhomogeneous Branching Diffusion Process under several conditions assuring existence and uniqueness of the diffusion part and nonexplosion of the branching process.
Pablo Olivares, Janko Hernandez
doaj   +1 more source

Uniform confidence bands for functions estimated nonparametrically with instrumental variables [PDF]

, 2009
This paper is concerned with developing uniform confidence bands for functions estimated nonparametrically with instrumental variables. We show that a sieve nonparametric instrumental variables estimator is pointwise asymptotically normally distributed ...
Joel L. Horowitz, Horowitz, J., Lee, S., Horowitz, Joel L., Lee, Sokbae, Sokbae (Simon) Lee   +5 more
core   +1 more source

Asymptotic normality of local linear regression estimator for mixtures with varying concentrations

Modern Stochastics: Theory and Applications
Finite mixtures with different regression models for different mixture components naturally arise in statistical analysis of biological and sociological data. In this paper a model of mixtures with varying concentrations is considered in which the mixing
Daniel Horbunov, Rostyslav Maiboroda
doaj   +1 more source

Relation between the phase-lag index and lagged coherence for assessing interactions in EEG and MEG data

NeuroImage: Reports, 2021
Over the last two decades, a large number of estimators have been proposed to assess brain connectivity from electroencephalography (EEG) and magnetoencephalography (MEG) data.
Rikkert Hindriks
doaj   +1 more source