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.
Second-order approximation of the entropy in nonlinear least-squares estimation
Seemingly unrelated regression models
The cross-covariance matrix of observation vectors in two linear statistical models need not be zero matrix. In such a case the problem is to find explicit expressions for the best linear unbiased estimators of both model parameters and estimators of variance components in the simplest structure of the covariance matrix. Univariate and multivariate forms of linear models are dealt with.
Selecting the Best Regression Equation via the P-Value of F-Test.
Selection in parametric models via some stepdown procedures
The paper considers the problem of consistent variable selection in parametic models with the use of stepdown multiple hypothesis procedures. Our approach completes the results of Bunea et al. [J. Statist. Plann. Inference 136 (2006)]. A simulation study supports the results obtained.