Regression type estimators of finite population variance under multiphase sampling.
Regular linear model with the nuisance parameters with constraints of the type I
Relative Measurement and Its Generalization in Decision Making. Why Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors. The Analytic Hierarchy/Network Process.
According to the great mathematician Henri Lebesgue, making direct comparisons of objects with regard to a property is a fundamental mathematical process for deriving measurements. Measuring objects by using a known scale first then comparing the measurements works well for properties for which scales of measurement exist. The theme of this paper is that direct comparisons are necessary to establish measurements for intangible properties that have no scales of measurement. In that case the value...
Remarks on algorithm 32
Repeated regression experiment and estimation of variance components
Ridge estimation of covariance matrix from data in two classes
This paper deals with the problem of estimating a covariance matrix from the data in two classes: (1) good data with the covariance matrix of interest and (2) contamination coming from a Gaussian distribution with a different covariance matrix. The ridge penalty is introduced to address the problem of high-dimensional challenges in estimating the covariance matrix from the two-class data model. A ridge estimator of the covariance matrix has a uniform expression and keeps positive-definite, whether...
Ridge Estimator Revisited
Bad conditioned matrix of normal equations in connection with small values of model parameters is a source of problems in parameter estimation. One solution gives the ridge estimator. Some modification of it is the aim of the paper. The behaviour of it in models with constraints is investigated as well.
Ridge least weighted squares
Robust estimation in a linear regression model
Robust Estimation of the Structural Errors-in-Variables Model.
Robust estimations in classical regression models versus robust estimations in fuzzy regression models
In this paper are presented two robust estimators of unknown fuzzy parameters in the fuzzy regression model and investigated the relationship between these robust estimators in the classical regression model and in the fuzzy regression model.
Robust estimators and their relations
Robust experimental design. A comment on Huber's result
Robust median estimator for generalized linear models with binary responses
The paper investigates generalized linear models (GLM's) with binary responses such as the logistic, probit, log-log, complementary log-log, scobit and power logit models. It introduces a median estimator of the underlying structural parameters of these models based on statistically smoothed binary responses. Consistency and asymptotic normality of this estimator are proved. Examples of derivation of the asymptotic covariance matrix under the above mentioned models are presented. Finally some comments...
Robust m-estimator of parameters in variance components model
It is shown that a method of robust estimation in a two way crossed classification mixed model, recently proposed by Bednarski and Zontek (1996), can be extended to a more general case of variance components model with commutative a covariance matrices.
Robust methods in exponential smoothing
Robust quasi-linear system identification
Robustness of the best linear unbiased estimator and predictor in linear regression models
If is shown that in linear regression models we do not make a great mistake if we substitute some sufficiently precise approximations for the unknown covariance matrix and covariance vector in the expressions for computation of the best linear unbiased estimator and predictor.
Robustness to non-normality of common tests for the many-sample location problem.