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Ridge estimation of covariance matrix from data in two classes

Yi Zhou, Bin Zhang (2024)

Applications of Mathematics

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

Lubomír Kubáček (2012)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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.

Robust median estimator for generalized linear models with binary responses

Tomáš Hobza, Leandro Pardo, Igor Vajda (2012)

Kybernetika

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

Roman Zmyślony, Stefan Zontek (2002)

Discussiones Mathematicae Probability and Statistics

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.

Seemingly unrelated regression models

Lubomír Kubáček (2013)

Applications of Mathematics

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.

Selection in parametric models via some stepdown procedures

Konrad Furmańczyk (2014)

Applicationes Mathematicae

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.

Selective F tests for sub-normal models

Célia Maria Pinto Nunes, João Tiago Mexia (2003)

Discussiones Mathematicae Probability and Statistics

F tests that are specially powerful for selected alternatives are built for sub-normal models. In these models the observation vector is the sum of a vector that stands for what is measured with a normal error vector, both vectors being independent. The results now presented generalize the treatment given by Dias (1994) for normal fixed-effects models, and consider the testing of hypothesis on the ordering of mean values and components.

Selective generalized F tests

C. Nunes, J. T. Mexia (2004)

Discussiones Mathematicae Probability and Statistics

Generalized F tests were introduced by Michalski and Zmyślony (1996) for variance components and later (1999) for linear functions of parameters in mixed linear models. We now use generalized polar coordinates to obtain, for the second case, tests that are more powerful for selected families of alternatives.

Currently displaying 501 – 520 of 657