Eliminating transformations for nuisance parameters in linear regression models with type I constraints
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica (2007)
- Volume: 46, Issue: 1, page 51-64
- ISSN: 0231-9721
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topKunderová, Pavla. "Eliminating transformations for nuisance parameters in linear regression models with type I constraints." Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica 46.1 (2007): 51-64. <http://eudml.org/doc/32461>.
@article{Kunderová2007,
abstract = {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.},
author = {Kunderová, Pavla},
journal = {Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica},
keywords = {regular linear regression model; nuisance parameters; BLUE; constraints; regular liner regression model; BLUE},
language = {eng},
number = {1},
pages = {51-64},
publisher = {Palacký University Olomouc},
title = {Eliminating transformations for nuisance parameters in linear regression models with type I constraints},
url = {http://eudml.org/doc/32461},
volume = {46},
year = {2007},
}
TY - JOUR
AU - Kunderová, Pavla
TI - Eliminating transformations for nuisance parameters in linear regression models with type I constraints
JO - Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
PY - 2007
PB - Palacký University Olomouc
VL - 46
IS - 1
SP - 51
EP - 64
AB - 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.
LA - eng
KW - regular linear regression model; nuisance parameters; BLUE; constraints; regular liner regression model; BLUE
UR - http://eudml.org/doc/32461
ER -
References
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