Regression type estimators of finite population variance under multiphase sampling.
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Pradhan, B.K. (2010)
Bulletin of the Malaysian Mathematical Sciences Society. Second Series
Pavla Kunderová (2001)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
Lubomír Kubáček (1984)
Mathematica Slovaca
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...
Agnieszka Kulawik, Stefan Zontek (2016)
Discussiones Mathematicae Probability and Statistics
Robust estimation presented in the following paper is based on Fisher consistent and Fréchet differentiable statistical functionals. The method has been used in the multivariate normal model with variance components [5]. To transfer the method to estimate vector of expectations and positive definite covariance matrix of the multivariate normal model it is required to express the covariance matrix as a linear combination of basic elements of the vector space of real, square and symmetric matrices....
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.
Eckhard Liebscher (2009)
Kybernetika
In the paper we investigate properties of maximum pseudo-likelihood estimators for the copula density and minimum distance estimators for the copula. We derive statements on the consistency and the asymptotic normality of the estimators for the parameters.
Serge Cohen, Xavier Guyon, Olivier Perrin, Monique Pontier (2006)
Annales de l'I.H.P. Probabilités et statistiques
Tian, Yongge, Takane, Yoshio (2007)
ELA. The Electronic Journal of Linear Algebra [electronic only]
G.S. Datta
Metrika
Jitka Zichová (2011)
Acta Universitatis Carolinae. Mathematica et Physica
Kalická, J. (1996)
Acta Mathematica Universitatis Comenianae. New Series
Wojciech Chojnacki (2008)
Annales Polonici Mathematici
Letting P(u,x) denote the regularised incomplete gamma function, it is shown that for each α ≥ 0, P(x,x+α) decreases as x increases on the positive real semi-axis, and P(x,x+α) converges to 1/2 as x tends to infinity. The statistical significance of these results is explored.
G. Beganu (2009)
RACSAM
Nesrin Güler, Melek Eriş Büyükkaya (2022)
Applications of Mathematics
In this paper, we consider a comparison problem of predictors in the context of linear mixed models. In particular, we assume a set of different seemingly unrelated linear mixed models (SULMMs) allowing correlations among random vectors across the models. Our aim is to establish a variety of equalities and inequalities for comparing covariance matrices of the best linear unbiased predictors (BLUPs) of joint unknown vectors under SULMMs and their combined model. We use the matrix rank and inertia...
Lubomír Kubáček (2006)
Applications of Mathematics
Some remarks to problems of point and interval estimation, testing and problems of outliers are presented in the case of multivariate regression model.
Lubomír Kubáček (1970)
Aplikace matematiky
Lubomír Kubáček (1990)
Mathematica Slovaca
Hilmar Drygas (2009)
Discussiones Mathematicae Probability and Statistics
This paper deals with an application of regression analysis to the regulation of the blood-sugar under diabetes mellitus. Section 2 gives a description of Gram-Schmidt orthogonalization, while Section 3 discusses the difference between Gauss-Markov estimation and Least Squares Estimation. Section 4 is devoted to the statistical analysis of the blood-sugar during the night. The response change of blood-sugar is explained by three variables: time, food and physical activity ("Bewegung"). At the beginning...
Tomáš Cipra (1985)
Kybernetika