 An efficient procedure for the avoidance of disconnected incomplete block designsJ.D. Godolphin and H.R. Warren Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 1134-1146 Abstract: Knowledge of the cardinality and the number of minimal rank reducing observation sets in experimental design is important information which makes a useful contribution to the statistician’s tool-kit to assist in the selection of incomplete block designs. Its prime function is to guard against choosing a design that is likely to be altered to a disconnected eventual design if observations are lost during the course of the experiment. A method is given for identifying these observation sets based on the concept of treatment separation, which is a natural approach to the problem and provides a vastly more efficient computational procedure than a standard search routine for rank reducing observation sets. The properties of the method are derived and the procedure is illustrated by four applications which have been discussed previously in the literature. Keywords: Connectivity; Incomplete block design; Missing values; Observation loss; Rank reducing observation sets; Robustness; Selective partitioning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:1134-1146 DOI: 10.1016/j.csda.2013.09.025 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.