The application of minimax fit in linear model and its extension in generalised linear models.
The closeness of the range of a probability on a certain system of random events -- an elementary proof.
The correloid
The effect of a single point on correlation and slope.
The efficiency of modified jackknife and ridge type regression estimators: a comparison.
The Frisch scheme in algebraic and dynamic identification problems
This paper considers the problem of determining linear relations from data affected by additive noise in the context of the Frisch scheme. The loci of solutions of the Frisch scheme and their properties are first described in the algebraic case. In this context two main problems are analyzed: the evaluation of the maximal number of linear relations compatible with data affected by errors and the determination of the linear relation actually linking the noiseless data. Subsequently the extension...
The full rank case for a linearizable model.
The generalized p-point method of estimation of regression coefficients
The LASSO estimator: Distributional properties
The least absolute shrinkage and selection operator (LASSO) is a popular technique for simultaneous estimation and model selection. There have been a lot of studies on the large sample asymptotic distributional properties of the LASSO estimator, but it is also well-known that the asymptotic results can give a wrong picture of the LASSO estimator's actual finite-sample behaviour. The finite sample distribution of the LASSO estimator has been previously studied for the special case of orthogonal models....
The least trimmed squares. Part I: Consistency
The consistency of the least trimmed squares estimator (see Rousseeuw [Rous] or Hampel et al. [HamRonRouSta]) is proved under general conditions. The assumptions employed in paper are discussed in details to clarify the consequences for the applications.
The least trimmed squares. Part II: -consistency
-consistency of the least trimmed squares estimator is proved under general conditions. The proof is based on deriving the asymptotic linearity of normal equations.
The least trimmed squares. Part III: Asymptotic normality
Asymptotic normality of the least trimmed squares estimator is proved under general conditions. At the end of paper a discussion of applicability of the estimator (including the discussion of algorithm for its evaluation) is offered.
The locally best estimators of the first and second order parameters in epoch regression models
The Locally MIMSQE of Nonnormal Error Variance in Quadratically Balanced Models.
The multiple regression model where independent variables are measured unprecisely.
The Occurrence of Outliers in the Explanatory Variable Considered in an Errors-in-Variables Framework.
The Stepwise Regression Algorithm Seen from the Statistician's Point of View
The two-dimensional linear relation in the errors-in-variables model with replication of one variable
We present a two-dimensional linear regression model where both variables are subject to error. We discuss a model where one variable of each pair of observables is repeated. We suggest two methods to construct consistent estimators: the maximum likelihood method and the method which applies variance components theory. We study asymptotic properties of these estimators. We prove that the asymptotic variances of the estimators of regression slopes for both methods are comparable.
The Type A Uncertainty
If in the model of measurement except useful parameters, which are to be determined, other auxiliary parameters occur as well, which were estimated from another experiment, then the type A and B uncertainties of measurement results must be taken into account. The type A uncertainty is caused by the new experiment and the type B uncertainty characterizes an accuracy of the parameters which must be used in estimation of useful parameters. The problem is to estimate of the type A uncertainty in the...
The unreplicated complex linear functional relationship model and its application.