Add to ORKGGrenander (1956) derived the maximum likelihood estimator (MLE) for a unimodal density ƒ. We obtain the asymptotic distribution of this estimator in this paper and we shall also prove that this estimator is consistent. The estimation problem is reduced at first to that of a stochastic process and the asymptotic distribution of MLE is obtained by means of theorems on convergence of distributions of stochastic processes
Two classes of unbiased estimators of the density function of ergodic distribution for the diffusion...
We consider maximum likelihood estimation of the parameters of a probability density which is zero f...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
Grenander (1956) derived the maximum likelihood estimator (MLE) for a unimodal density ƒ. We ob...
This paper shows that if the likelihood functions are unimodal then the distributions of maximum lik...
This paper formulates the nonparametric maximum likelihood es-timation of probability measures and g...
In this paper, we study, in some new ways, the estimation of unimodal densities. Several methods for...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
In this paper we give a formula for the exact density of the MAXIMUM LIKELIHOOD ESTIMATOR (MLE) unde...
This paper is concerned with the estimation of a parameter of a stochastic process on the basis of a...
We study the rates of convergence of the maximum likelihood esti-mator (MLE) and posterior distribut...
AbstractMaximum likelihood and approximate maximum likelihood estimates of parameters of random proc...
In this paper, we shall investigate a problem analogous to the one treated in Prakasa Rao [6]. We sh...
The asymptotic theory of estimators obtained from estimating functions is re-viewed and some new res...
Two classes of unbiased estimators of the density function of ergodic distribution for the diffusion...
We consider maximum likelihood estimation of the parameters of a probability density which is zero f...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
Grenander (1956) derived the maximum likelihood estimator (MLE) for a unimodal density ƒ. We ob...
This paper shows that if the likelihood functions are unimodal then the distributions of maximum lik...
This paper formulates the nonparametric maximum likelihood es-timation of probability measures and g...
In this paper, we study, in some new ways, the estimation of unimodal densities. Several methods for...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
In this paper we give a formula for the exact density of the MAXIMUM LIKELIHOOD ESTIMATOR (MLE) unde...
This paper is concerned with the estimation of a parameter of a stochastic process on the basis of a...
We study the rates of convergence of the maximum likelihood esti-mator (MLE) and posterior distribut...
AbstractMaximum likelihood and approximate maximum likelihood estimates of parameters of random proc...
In this paper, we shall investigate a problem analogous to the one treated in Prakasa Rao [6]. We sh...
The asymptotic theory of estimators obtained from estimating functions is re-viewed and some new res...
Two classes of unbiased estimators of the density function of ergodic distribution for the diffusion...
We consider maximum likelihood estimation of the parameters of a probability density which is zero f...
The transition density of a diffusion process does not admit an explicit expression in general, whic...