Regression type estimators of finite population variance under multiphase sampling.
Regular linear model with the nuisance parameters with constraints of the type I
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...
Robust estimation in the multivariate normal model
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....
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.
Semiparametric estimation of the parameters of multivariate copulas
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.
Singularity functions for fractional processes : application to the fractional brownian sheet
Some algebraic and statistical properties of WLSEs under a general growth curve model.
Some Applications of Anderson's Inequality to Some Optimality Criteria of the Generalized Least Squares Estimator.
Some applications of time series models to financial data
Some limit properties of an approximate least squares estimator in a nonlinear regression model with correlated nonzero mean errors.
Some monotonicity and limit results for the regularised incomplete gamma function
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.
Some properties of the best linear unbiased estimators in multivariate growth curve models.
Some remarks on comparison of predictors in seemingly unrelated linear mixed models
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...
Some remarks to multivariate regression model
Some remarks to problems of point and interval estimation, testing and problems of outliers are presented in the case of multivariate regression model.
Some statistical aspects of the estimation of parameters of a linear conform transformation
Special structures of mixed linear models with nuisance parameters
Statistical analysis of diabetes mellitus
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...
Statistical analysis of multiple moving average processes using periodicity