Errors estimation and the asymptotic distribution of probabilistic estimates.
Estimación de correlaciones utilizando envolturas convexas.
En el presente trabajo se realiza un estudio de la envoltura convexa de una muestra normal bivariante, analizando la distribución de la pendiente de sus aristas. En base a ello se propone un estimador del coeficiente de correlación de la población, investigando algunas propiedades del mismo.
Estimates for distributions of sums of random variables with subexponential distributions.
Estimates of Relative Risk.
Estimates of reliability for the normal distribution
The minimum variance unbiased, the maximum likelihood, the Bayes, and the naive estimates of the reliability function of a normal distribution are studied. Their asymptotic normality is proved and asymptotic expansions for both the expectation and the mean squared error are derived. The estimates are then compared using the concept of deficiency. In the end an extensive Monte Carlo study of the estimates in small samples is given.
Estimating from cross-sectional categorical data subject to misclassification and double sampling: moment-based, maximum likelihood and quasi-likelihood approaches.
Estimating normal density and normal distribution function: is Kolmogorov's estimator admissible?
The statistical estimation problem of the normal distribution function and of the density at a point is considered. The traditional unbiased estimators are shown to have Bayes nature and admissibility of related generalized Bayes procedures is proved. Also inadmissibility of the unbiased density estimator is demonstrated.
Estimating quantiles with Linex loss function. Applications to VaR estimation
Sometimes, e.g. in the context of estimating VaR (Value at Risk), underestimating a quantile is less desirable than overestimating it, which suggests measuring the error of estimation by an asymmetric loss function. As a loss function when estimating a parameter θ by an estimator T we take the well known Linex function exp{α(T-θ)} - α(T-θ) - 1. To estimate the quantile of order q ∈ (0,1) of a normal distribution N(μ,σ), we construct an optimal estimator in the class of all estimators of the form...
Estimating Survivor Function Using Optimally Selected Order Statistics.
Estimating the Variance of the Ratio Estimator for the Midzuno-Sen Sampling Scheme.
Estimation and prediction in regression models with random explanatory variables [Book]
Estimation and tests of the discrete probability law based on the empirical generating function, (two dimensional case).
Estimation de la variance, dans un modèle classique, si les coefficients d'aplatissement des variables sont connus
Estimation des paramètres d'un modèle de Rasch mixte par la méthode GEE : application en qualité de vie
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.