Each week, we will be publishing layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.
The article featured today is from the Canadian Journal of Statistics, with the full article now available to read here.
Khodsiani, R. and Pooladsaz, S. (2020), Optimal balanced block designs for correlated observations. Can J Statistics, 48: 596-604. doi:10.1002/cjs.11549
The block experiments have been widely used in sciences, medicine and engineering. Blocking is used to remove the effects of a few of the most important nuisance variables. When the observations in a block is correlated, we may find a special structure for the correlation between observations.
Some different correlation structures have been considered by many researchers. Hub structure is one of them that is a fixed correlation between hub observation (typically the observation in the first unit) and each of the other observations in each block.
In agriculture, network security, biology, industry, finance and social networks experiments, the hub correlation structure can be considered.
Many researchers would like to find a design which has the most information of experiment. This design is called the best design or optimal design. There are some optimality criteria. If a design be optimal under all optimality criteria (e.g. A, D, E, etc.), then it is called universal optimal.
In this paper, we obtain the universally optimal designs under hub correlation with different parameter values.