On estimable and locally-estimable functions in the non-linear regression model
On estimation in the multidimensional Gaussian model
On estimation of a covariance function of stationary errors in a nonlinear regression model.
On estimation of multiple regression coefficients by the p-point method
On estimation of parameters in linear models
On estimation of parameters in the bivariate linear errors-in-variables model
We discuss some methods of estimation in bivariate errors-in-variables linear models. We also suggest a method of constructing consistent estimators in the case when the error disturbances have the normal distribution with unknown parameters. It is based on the theory of estimating variance components in linear models. A simulation study is presented which compares this estimator with the maximum likelihood one.
On Fourier coefficient estimators consistent in the mean-square sense
The properties of two recursive estimators of the Fourier coefficients of a regression function with respect to a complete orthonormal system of bounded functions (ek) , k=1,2,..., are considered in the case of the observation model , i=1,...,n , where are independent random variables with zero mean and finite variance, , i=1,...,n, form a random sample from a distribution with density ϱ =1/(b-a) (uniform distribution) and are independent of the errors , i=1,...,n . Unbiasedness and mean-square...
On geometry of the set of admissible quadratic estimators of quadratic functions of normal parameters
We consider the problem of admissible quadratic estimation of a linear function of μ² and σ² in n dimensional normal model N(Kμ,σ²Iₙ) under quadratic risk function. After reducing this problem to admissible estimation of a linear function of two quadratic forms, the set of admissible estimators are characterized by giving formulae on the boundary of the set D ⊂ R² of components of the two quadratic forms constituting the set of admissible estimators. Different shapes and topological properties of...
On Kaiman Filtering, Posterior Mode Estimation and Fisher Scoring in Dynamic Exponential Family Regression.
On Limits of Uniquely Best Linear Estimators.
On maximum likelihood estimation in mixed normal models with two variance components
In the paper we deal with the problem of parameter estimation in the linear normal mixed model with two variance components. We present solutions to the problem of finding the global maximizer of the likelihood function and to the problem of finding the global maximizer of the REML likelihood function in this model.
On multiple test procedures
On non-nested regression models
A generalization of a test for non-nested models in linear regression is derived for the case when there are several regression models with more regressors.
On non-normal asymptotic behavior of optimal solutions for stochastic programming problems and on related problems of mathematical statistics
On one approach to local surface smoothing
A bicubic model for local smoothing of surfaces is constructed on the base of pivot points. Such an approach allows reducing the dimension of matrix of normal equations more than twice. The model enables to increase essentially the speed and stability of calculations. The algorithms, constructed by the aid of the offered model, can be used both in applications and the development of global methods for smoothing and approximation of surfaces.
On one multivariate linear model with nuisance parameters
On optimum experimental design for ridge estimates
On parameter-effects arrays in non-linear regression models
Formulas for a new three- and four-dimensional parameter-effects arrays corresponding to transformations of parameters in non-linear regression models are given. These formulae make the construction of the confidence regions for parameters easier. An example is presented which shows that some care is necessary when a new array is computed.
On perturbations of strongly admissible prior distributions
On Regression Models with Non-Square Integrable Martingale-Like Errors