Optimal designs for collapsed homogeneous linear model
Xuebo Sun and
Yingnan Guan
Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 8, 2303-2329
Abstract:
In our research work, the problem of how to construct the optimal design of collapse mixture model has been further explored and studied, and some new progress has been made. In this paper an abstract optimal criterion which is called normality optimality criteria is proposed by specifying some features of its equivalent matrix and invariant. And the optimality criteria, such as D-, A-, and R- etc., are proved to be normality optimality criteria. Meanwhile, the concept of collapsed homogeneous linear model is also proposed, and an inequality related to the collapsed homogeneous linear model is also proved. For multi mixture experiment, a concept of optimal weights for collapsed mixture model is also proposed first. For the so-called normality optimality criterion, an optimal weight for the collapsed homogeneous linear model is obtained by using these concepts and inequality mentioned above. The results obtained in this paper are not only applicable to the optimality criteria, such as D-, A-, and R- etc., but also applicable to the normal optimality criteria satisfying certain conditions.
Date: 2022
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2021.1963777 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:51:y:2022:i:8:p:2303-2329
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2021.1963777
Access Statistics for this article
Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe
More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().