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Iterative estimators of parameters in linear models with partially variant coefficients

100%
Hu S., Meinke K., Chen R., Huajiang O.
International Journal of Applied Mathematics and Computer Science
|
2007
|
tom 17
|
nr 2
179-187
EN
A new kind of linear model with partially variant coefficients is proposed and a series of iterative algorithms are introduced and verified. The new generalized linear model includes the ordinary linear regression model as a special case. The iterative algorithms efficiently overcome some difficulties in computation with multidimensional inputs and incessantly appending parameters. An important application is described at the end of this article, which shows that this new model is reasonable and applicable in practical fields.
2
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Likelihood and parametric heteroscedasticity in normal connected linear models

75%
Mexia J., Real P.
Discussiones Mathematicae Probability and Statistics
|
2000
|
tom 20
|
nr 2
177-188
EN
A linear model in which the mean vector and covariance matrix depend on the same parameters is connected. Limit results for these models are presented. The characteristic function of the gradient of the score is obtained for normal connected models, thus, enabling the study of maximum likelihood estimators. A special case with diagonal covariance matrix is studied.
3
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Decomposing matrices with Jerzy K. Baksalary

63%
Isotalo J., Puntanen S., Styan G.
Discussiones Mathematicae Probability and Statistics
|
2008
|
tom 28
|
nr 1
91-111
EN
In this paper we comment on some papers written by Jerzy K. Baksalary. In particular, we draw attention to the development process of some specific research ideas and papers now that some time, more than 15 years, has gone after their publication.
4

Numerical methods for linear minimax estimation

63%
Gaffke N., Heiligers B.
Discussiones Mathematicae Probability and Statistics
|
2000
|
tom 20
|
nr 1
51-62
EN
We discuss two numerical approaches to linear minimax estimation in linear models under ellipsoidal parameter restrictions. The first attacks the problem directly, by minimizing the maximum risk among the estimators. The second method is based on the duality between minimax and Bayes estimation, and aims at finding a least favorable prior distribution.
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