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62-XX Statistics
62Fxx Parametric inference
62F30 Inference under constraints

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Eliminating transformations for nuisance parameters in linear regression models with type I constraints

Pavla Kunderová (2007)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

The linear regression model in which the vector of the first order parameter is divided into two parts: to the vector of the useful parameters and to the vector of the nuisance parameters is considered. The type I constraints are given on the useful parameters. We examine eliminating transformations which eliminate the nuisance parameters without loss of information on the useful parameters.

Estimation in universal models with restrictions

Eva Fišerová (2004)

Discussiones Mathematicae Probability and Statistics

In modelling a measurement experiment some singularities can occur even if the experiment is quite standard and simple. Such an experiment is described in the paper as a motivation example. It is presented in the papar how to solve these situations under special restrictions on model parameters. The estimability of model parameters is studied and unbiased estimators are given in explicit forms.

Currently displaying 1 – 2 of 2

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