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Construction of Resolvable Spatial Row–Column Designs
Author(s) -
Williams E. R.,
John J. A.,
Whitaker D.
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00393.x
Subject(s) - column (typography) , variance (accounting) , computer science , block (permutation group theory) , spatial analysis , spatial dependence , autoregressive model , field (mathematics) , algorithm , blocking (statistics) , mathematics , statistics , geometry , connection (principal bundle) , accounting , pure mathematics , business
Summary Resolvable row–column designs are widely used in field trials to control variation and improve the precision of treatment comparisons. Further gains can often be made by using a spatial model or a combination of spatial and incomplete blocking components. Martin, Eccleston, and Gleeson (1993, Journal of Statistical Planning and Inference 34, 433–450) presented some general principles for the construction of robust spatial block designs which were addressed by spatial designs based on the linear variance (LV) model. In this article we define the two‐dimensional form of the LV model and investigate extensions of the Martin et al. principles for the construction of resolvable spatial row–column designs. The computer construction of efficient spatial designs is discussed and some comparisons made with designs constructed assuming an autoregressive variance structure.