Trend resistant balanced bipartite block designs

Seema Jaggi, Kader Ali Sarkar, Arpan Bhowmik, Eldho Varghese (), Cini Varghese and Anindita Datta
Additional contact information
Seema Jaggi: Indian Council of Agricultural Research
Kader Ali Sarkar: ICAR-Indian Agricultural Statistics Research Institute
Arpan Bhowmik: ICAR-Indian Agricultural Statistics Research Institute
Eldho Varghese: ICAR-Indian Agricultural Statistics Research Institute
Cini Varghese: ICAR-Indian Agricultural Statistics Research Institute
Anindita Datta: ICAR-Indian Agricultural Statistics Research Institute

Statistical Methods & Applications, 2023, vol. 32, issue 1, No 10, 235 pages

Abstract: Abstract Balanced Bipartite Block (BBPB) designs resistant against the trend are used when the interest of the experimenter is in making comparisons between two sets of treatments that are disjoint, and there is the presence of systematic trend within a block. This paper deals with the bipartite block model incorporating trend component. The general methodology has been described related to BBPB designs incorporating trend effect. The conditions for a BBPB design to be trend resistant are also obtained. Further, methods of constructing trend resistant BBPB designs are discussed. The designs so obtained are trend resistant and are more efficient for estimating the contrasts pertaining to two treatments from different sets.

Keywords: Block design; Balanced bipartite; Trend resistant design (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-022-00652-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:stmapp:v:32:y:2023:i:1:d:10.1007_s10260-022-00652-3

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-022-00652-3

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().