Alternative parametric estimation in the exponential case under random censorship
An admissible estimator of a lower-bounded scale parameter under squared-log error loss function
Estimation in truncated parameter space is one of the most important features in statistical inference, because the frequently used criterion of unbiasedness is useless, since no unbiased estimator exists in general. So, other optimally criteria such as admissibility and minimaxity have to be looked for among others. In this paper we consider a subclass of the exponential families of distributions. Bayes estimator of a lower-bounded scale parameter, under the squared-log error loss function with...
An alternative analysis of variance.
The one-way analysis of variance is a staple of elementary statistics courses. The hypothesis test of homogeneity of the means encourages the use of the selected-model based estimators which are usually assessed without any regard for the uncertainty about the outcome of the test. We expose the weaknesses of such estimators when the uncertainty is taken into account, as it should be, and propose synthetic estimators as an alternative.
An empirical investigation of Cressie and Read tests for the hypothesis of independence in three-way contingency tables
An exploratory canonical analysis approach for multinomial populations based on the -divergence measure
In this paper we consider an exploratory canonical analysis approach for multinomial population based on the -divergence measure. We define the restricted minimum -divergence estimator, which is seen to be a generalization of the restricted maximum likelihood estimator. This estimator is then used in -divergence goodness-of-fit statistics which is the basis of two new families of statistics for solving the problem of selecting the number of significant correlations as well as the appropriateness...
An improper Cramer-Rao lower bound
Analyse de mélanges gaussiens pour de petits échantillons : application à la cinématique stellaire
Analysis on the individual efficiency prediction in the composed error frontier model. A Monte Carlo study.
This study seeks to analyse some important questions related to the Stochastic Frontier Model, such as the method proposed by Jondrow et al (1982) to separate the error term into its two components, and the measure of efficiency given by Timmer (1971). To this purpose, a Monte Carlo experiment has been carried out using the Half-Normal and Normal-Exponential specifications throughout the rank of the γ parameter. The estimation errors have been eliminated, so that the intrinsic variability of the...
Approximate bias for first-order autoregressive model with uniform innovations. Small sample case
The first-order autoregressive model with uniform innovations is considered. The approximate bias of the maximum likelihood estimator (MLE) of the parameter is obtained. Also, a formula for the approximate bias is given when a single outlier occurs at a specified time with a known amplitude. Simulation procedures confirm that our formulas are suitable. A small sample case is considered only.
Asymptotic comparison of maximum likelihood and a rank estimate in simple linear regression model
Asymptotic Properties of Minimum Contrast Estimators for Parameters of Boolean Models.
Asymptotically Efficient Recursive Estimation for Incomplete Data Models Using the Observed Information.
Asymptotically minimax estimation of a constrained Poisson vector via polydisc transforms
Asymptotics for robust MOSUM
Bad luck in quadratic improvement of the linear estimator in a special linear model
The paper concludes our investigations in looking for the locally best linear-quadratic estimators of mean value parameters and of the covariance matrix elements in a special structure of the linear model (2 variables case) where the dispersions of the observed quantities depend on the mean value parameters. Unfortunately there exists no linear-quadratic improvement of the linear estimator of mean value parameters in this model.
Bayes unbiased estimators of parameters of linear trend with autoregressive errors
The method of least wquares is usually used in a linear regression model for estimating unknown parameters . The case when is an autoregressive process of the first order and the matrix corresponds to a linear trend is studied and the Bayes approach is used for estimating the parameters . Unbiased Bayes estimators are derived for the case of a small number of observations. These estimators are compared with the locally best unbiased ones and with the usual least squares estimators.
Bayesian estimation of AR(1) models with uniform innovations
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.
Bayesian joint modelling of the mean and covariance structures for normal longitudinal data.
We consider the joint modelling of the mean and covariance structures for the general antedependence model, estimating their parameters and the innovation variances in a longitudinal data context. We propose a new and computationally efficient classic estimation method based on the Fisher scoring algorithm to obtain the maximum likelihood estimates of the parameters. In addition, we also propose a new and innovative Bayesian methodology based on the Gibbs sampling, properly adapted for longitudinal...
Bayesian reference analysis for proportional hazards model of random censorship with Weibull distribution
This article deals with the objective Bayesian analysis of random censorship model with informative censoring using Weibull distribution. The objective Bayesian analysis has a long history from Bayes and Laplace through Jeffreys and is reaching the level of sophistication gradually. The reference prior method of Bernardo is a nice attempt in this direction. The reference prior method is based on the Kullback-Leibler divergence between the prior and the corresponding posterior distribution and easy...
Best linear plus quadratic unbiased estimation of parameters in mixed linear models