On -estimators viewed as -estimators
On maximum likelihood estimation in mixed normal models with two variance components
In the paper we deal with the problem of parameter estimation in the linear normal mixed model with two variance components. We present solutions to the problem of finding the global maximizer of the likelihood function and to the problem of finding the global maximizer of the REML likelihood function in this model.
On numerical evaluation of maximum-likelihood estimates for finite mixtures of distributions
On optimality of the LR tests in the sense of exact slopes. II. Application to individual distributions
On optimality of the LR tests in the sense of exact slopes. I. General case
On robust estimation in the simplest exponential model
On some distributiions involving general functions
On some general distributions in terms of generalized functions
On some properties of ML and REML estimators in mixed normal models with two variance components
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 random...
On testing variance components in unbalanced mixed linear model
The paper presents some approximate and exact tests for testing variance components in general unbalanced mixed linear model. It extends the results presented by Seifert (1992) with emphasis on the computational aspects of the problem.
On the Bayes estimators of the parameters of inflated modified power series distributions
In this paper, we study the class of inflated modified power series distributions (IMPSD) where inflation occurs at any of support points. This class includes among others the generalized Poisson,the generalized negative binomial and the lost games distributions. We derive the Bayes estimators of parameters for these distributions when a parameter of inflation is known. First, we take as the prior distribution the uniform, Beta and Gamma distribution. In the second part of this paper, the prior...
On the compound Poisson-gamma distribution
The compound Poisson-gamma variable is the sum of a random sample from a gamma distribution with sample size an independent Poisson random variable. It has received wide ranging applications. In this note, we give an account of its mathematical properties including estimation procedures by the methods of moments and maximum likelihood. Most of the properties given are hitherto unknown.
On the convergence of Bhattacharyya bounds in the multiparameter case
On the convergence of the Bhattacharyya bounds in the multiparametric case
Shanbhag (1972, 1979) showed that the diagonality of the Bhattacharyya matrix characterizes the set of normal, Poisson, binomial, negative binomial, gamma or Meixner hypergeometric distributions. In this note, using Shanbhag's techniques, we show that if a certain generalized version of the Bhattacharyya matrix is diagonal, then the bivariate distribution is either normal, Poisson, binomial, negative binomial, gamma or Meixner hypergeometric. Bartoszewicz (1980) extended the result of Blight and...
On the Equivalence between Orthogonal Regression and Linear Model with Type-II Constraints
Orthogonal regression, also known as the total least squares method, regression with errors-in variables or as a calibration problem, analyzes linear relationship between variables. Comparing to the standard regression, both dependent and explanatory variables account for measurement errors. Through this paper we shortly discuss the orthogonal least squares, the least squares and the maximum likelihood methods for estimation of the orthogonal regression line. We also show that all mentioned approaches...
On the Estimation of Data Parameters for Economic Optimum X-Charts.
On the Estimation of Pr {Y < X} for Two Parameter Exponential Distribution.
On the estimation of the autocorrelation function
The autocorrelation function has a very important role in several application areas involving stochastic processes. In fact, it assumes the theoretical base for Spectral analysis, ARMA (and generalizations) modeling, detection, etc. However and as it is well known, the results obtained with the more current estimates of the autocorrelation function (biased or not) are frequently bad, even when we have access to a large number of points. On the other hand, in some applications, we need to perform...
On the existence and uniqueness of the maximum likelihood estimators of normal and lognormal population parameters with grouped data.
On the General Problem of Adjustment of Measured Values