Estimation in a special structure of the linear model
Estimation in the Pareto Distribution.
Estimation in universal models with restrictions
In modelling a measurement experiment some singularities can occur even if the experiment is quite standard and simple. Such an experiment is described in the paper as a motivation example. It is presented in the papar how to solve these situations under special restrictions on model parameters. The estimability of model parameters is studied and unbiased estimators are given in explicit forms.
Estimation non paramétrique de l'espérance et de la variance de la loi de reproduction d'un processus de ramification
Estimation of a quadratic function of the parameter of the mean in a linear model
The paper deals with an optimal estimation of the quadratic function , where is a known matrix, in the model . The distribution of is assumed to be symmetric and to have a finite fourth moment. An explicit form of the best unbiased estimator is given for a special case of the matrix .
Estimation of dispersion in nonlinear regression models with constraints
Dispersion of measurement results is an important parameter that enables us not only to characterize not only accuracy of measurement but enables us also to construct confidence regions and to test statistical hypotheses. In nonlinear regression model the estimator of dispersion is influenced by a curvature of the manifold of the mean value of the observation vector. The aim of the paper is to find the way how to determine a tolerable level of this curvature.
Estimation of Error Variance in One-Way Random Model.
Estimation of log-linear-binomial distribution with applications.
Estimation of parameters in a network reliability model with spatial dependence
An iterative method based on a fixed-point property is proposed for finding maximum likelihood estimators for parameters in a model of network reliability with spatial dependence. The method is shown to converge at a geometric rate under natural conditions on data.
Estimation of parameters in a network reliability model with spatial dependence
An iterative method based on a fixed-point property is proposed for finding maximum likelihood estimators for parameters in a model of network reliability with spatial dependence. The method is shown to converge at a geometric rate under natural conditions on data.
Estimation of parameters in exponential autoregressive models.
This paper discusses the estimation of parameters in second order exponential autoregressive models.
Estimation of parameters of mean and variance in two-stage linear models
The paper deals with the estimation of unknown vector parameter of mean and scalar parameters of variance as well in two-stage linear model, which is a special type of mixed linear model. The necessary and sufficient condition for the existence of uniformly best unbiased estimator of parameter of means is given. The explicite formulas for these estimators and for the estimators of the parameters of variance as well are derived.
Estimation of parameters of RCA with exponential marginals.
Estimation of polynomials in the regression model
Let be an -dimensional random vector which is distributed. A minimum variance unbiased estimator is given for provided is an unbiasedly estimable functional of an unknown -dimensional parameter .
Estimation of P{Y
Estimation of reliability in the exponential case (I)
Estimation of reliability in the exponential case (II)
Estimation of Symmetrie Functions of a Finite Population.
Estimation of the first order parameters in the twoepoch linear model
The linear regression model, where the mean value parameters are divided into stable and nonstable part in each of both epochs of measurement, is considered in this paper. Then, equivalent formulas of the best linear unbiased estimators of this parameters in both epochs using partitioned matrix inverse are derived.
Estimation of the generalized variance in a bivariate normal distribution from an incomplete sample
The aim of the paper is estimation of the generalized variance of a bivariate normal distribution in the case of a sample with missing observations. The estimator based on all available observations is compared with the estimator based only on complete pairs of observations.