scholarly journals Asymptotic normality of discretized maximum likelihood estimator for drift parameter in homogeneous diffusion model

2015 ◽  
Vol 2 (1) ◽  
pp. 17-28
Author(s):  
Kostiantyn Ralchenko
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Normality ◽  
Maximum Likelihood Estimator ◽  
Diffusion Model ◽  
Likelihood Estimator ◽  
Drift Parameter

Speed of Convergence of the Maximum Likelihood Estimator in the Ornstein-Uhlenbeck Process

1995 ◽  
Vol 45 (3-4) ◽  
pp. 245-252 ◽  
Author(s):  
J. P. N. Bishwal ◽  
Arup Bose
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Subject Classification ◽  
Speed Of Convergence ◽  
Primary 62F12 ◽  
Likelihood Estimator ◽  
Uhlenbeck Process ◽  
Ornstein Uhlenbeck Process ◽  
Drift Parameter

Berry-Bsseen bounds with random norming and Jario deviation probabilities arc derived for the maximum likelihood estimator of the drift parameter in tho Ornstoin-Uhlenbeck proccss. AMS (1991) Subject Classification: Primary 62F12, 62M05 Secondary 60FOS, 60F10

Locally asymptotic normality of Gibbs models on a lattice

1984 ◽  
Vol 16 (03) ◽  
pp. 585-602
Author(s):  
Shigeru Mase
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Normality ◽  
Maximum Likelihood Estimator ◽  
Estimation Procedure ◽  
Point Of View ◽  
Normal Families ◽  
Point Pattern ◽  
Likelihood Estimator ◽  
Asymptotic Point ◽  
Statistical Estimation Problem

We consider the statistical estimation problem of potential functions of Gibbs models on the plane lattice. We assume that the area on which a random point pattern is observed is sufficiently large and take an asymptotic point of view. The main result is to show the locally asymptotic normality of the Gibbs model under certain conditions. From this result we can show the optimality of the maximum likelihood estimator employing known results about locally asymptotic normal families, though a practical computation of the maximum likelihood estimator presents difficulties. An estimation procedure based on the method of moments is also proposed.

Locally asymptotic normality of Gibbs models on a lattice

1984 ◽  
Vol 16 (3) ◽  
pp. 585-602 ◽  
Author(s):  
Shigeru Mase
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Normality ◽  
Maximum Likelihood Estimator ◽  
Estimation Procedure ◽  
Point Of View ◽  
Normal Families ◽  
Point Pattern ◽  
Likelihood Estimator ◽  
Asymptotic Point ◽  
Statistical Estimation Problem

We consider the statistical estimation problem of potential functions of Gibbs models on the plane lattice. We assume that the area on which a random point pattern is observed is sufficiently large and take an asymptotic point of view. The main result is to show the locally asymptotic normality of the Gibbs model under certain conditions. From this result we can show the optimality of the maximum likelihood estimator employing known results about locally asymptotic normal families, though a practical computation of the maximum likelihood estimator presents difficulties. An estimation procedure based on the method of moments is also proposed.

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