Displaying similar documents to “On Weak Convergence of Parameter Estimators of General Statistical Parametric Models”

Using auxiliary information in statistical function estimation

Sergey Tarima, Dmitri Pavlov (2005)

ESAIM: Probability and Statistics

Similarity:

In many practical situations sample sizes are not sufficiently large and estimators based on such samples may not be satisfactory in terms of their variances. At the same time it is not unusual that some auxiliary information about the parameters of interest is available. This paper considers a method of using auxiliary information for improving properties of the estimators based on a current sample only. In particular, it is assumed that the information is available as a number of...

The use of third-order moments in structural models.

Erik Meijer, Ab Mooijart (1994)

Qüestiió

Similarity:

Structural models are usually estimated using only second order moments (covariances or correlations). When variables are nor multivariate normally distributed, however, methods that also fit higher order moments, such as skewnesses, are theoretically asymptotically preferable. This article reports result from a Monte Carlo simulation study in which estimators that fit both second-order moments and third-order moments are compared with estimators that fit only second-order moments. ...

Bayesian like R- and M- estimators of change points

Jaromír Antoch, Marie Husková (2000)

Discussiones Mathematicae Probability and Statistics

Similarity:

The purpose of this paper is to study Bayesian like R- and M-estimators of change point(s). These estimators have smaller variance than the related argmax type estimators. Confidence intervals for the change point based on the exchangeability arguments are constructed. Finally, theoretical results are illustrated on the real data set.

On some properties of ML and REML estimators in mixed normal models with two variance components

Stanisław Gnot, Andrzej Michalski, Agnieszka Urbańska-Motyka (2004)

Discussiones Mathematicae Probability and Statistics

Similarity:

In the paper, the problem of estimation of variance components σ₁² and σ₂² by using the ML-method and REML-method in a normal mixed linear model 𝒩 {Y,E(Y) = Xβ, Cov(Y) = σ₁²V + σ₂²Iₙ} is considered. This paper deal with properties of estimators of variance components, particularly when an explicit form of these estimators is unknown. The conditions when the ML and REML estimators can be expressed in explicit forms are given, too. The simulation study for one-way classification unbalanced...

Minimum disparity estimators for discrete and continuous models

María Luisa Menéndez, Domingo Morales, Leandro Pardo, Igor Vajda (2001)

Applications of Mathematics

Similarity:

Disparities of discrete distributions are introduced as a natural and useful extension of the information-theoretic divergences. The minimum disparity point estimators are studied in regular discrete models with i.i.d. observations and their asymptotic efficiency of the first order, in the sense of Rao, is proved. These estimators are applied to continuous models with i.i.d. observations when the observation space is quantized by fixed points, or at random, by the sample quantiles...

On decompositions of estimators under a general linear model with partial parameter restrictions

Bo Jiang, Yongge Tian, Xuan Zhang (2017)

Open Mathematics

Similarity:

A general linear model can be given in certain multiple partitioned forms, and there exist submodels associated with the given full model. In this situation, we can make statistical inferences from the full model and submodels, respectively. It has been realized that there do exist links between inference results obtained from the full model and its submodels, and thus it would be of interest to establish certain links among estimators of parameter spaces under these models. In this...

Bayesian estimation of AR(1) models with uniform innovations

Hocine Fellag, Karima Nouali (2005)

Discussiones Mathematicae Probability and Statistics

Similarity:

The first-order autoregressive model with uniform innovations is considered. In this paper, we propose a family of BAYES estimators based on a class of prior distributions. We obtain estimators of the parameter which perform better than the maximum likelihood estimator.