Criteria for optimal design of small-sample experiments with correlated observations

Title:

Criteria for optimal design of small-sample experiments with correlated observations

Creator:

Pázman, Andrej

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:4af11b52-4ebe-4f27-941d-b9f47d31c0d5
uuid:4af11b52-4ebe-4f27-941d-b9f47d31c0d5

Subject:

optimal design, correlated observations, random field, spatial statistics, and information matrix

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

We consider observations of a random process (or a random field), which is modeled by a nonlinear regression with a parametrized mean (or trend) and a parametrized covariance function. Optimality criteria for parameter estimation are to be based here on the mean square errors (MSE) of estimators. We mention briefly expressions obtained for very small samples via probability densities of estimators. Then we show that an approximation of MSE via Fisher information matrix is possible, even for small or moderate samples, when the errors of observations are normal and small. Finally, we summarize some properties of optimality criteria known for the noncorrelated case, which can be transferred to the correlated case, in particular a recently published concept of universal optimality.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Kybernetika | 2007 Volume:43 | Number:4

Harvested from:

CDK

Metadata only:

false