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Please use this identifier to cite or link to this item: https://ir.iimcal.ac.in:8443/jspui/handle/123456789/1279
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dc.contributor.authorAlqallaf, Fatemah A.
dc.contributor.authorHuda, Shahariar
dc.contributor.authorMukerjee, Rahul
dc.date.accessioned2021-08-26T06:05:22Z-
dc.date.available2021-08-26T06:05:22Z-
dc.date.issued2019
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85061457322&doi=10.1016%2fj.spl.2019.01.027&partnerID=40&md5=b8dde9ac4f7c9dbdda8c31787d831756
dc.identifier.urihttps://ir.iimcal.ac.in:8443/jspui/handle/123456789/1279-
dc.descriptionAlqallaf, Fatemah A., Department of Statistics and OR, Faculty of Science, Kuwait University, P.O. Box-5969, Safat, 13060, Kuwait; Huda, Shahariar, Department of Statistics and OR, Faculty of Science, Kuwait University, P.O. Box-5969, Safat, 13060, Kuwait; Mukerjee, Rahul, Indian Institute of Management Calcutta, Joka, Diamond Harbour Road, Kolkata, 700104, India
dc.descriptionISSN/ISBN - 01677152
dc.descriptionpp.55-62
dc.descriptionDOI - 10.1016/j.spl.2019.01.027
dc.description.abstractA randomization-based theory of causal inference from strip-plot designs is developed. For any treatment contrast, we propose an unbiased estimator, work out its sampling variance, and obtain a conservative variance estimator which is shown to enjoy a minimaxity property.
dc.publisherSCOPUS
dc.publisherStatistics and Probability Letters
dc.publisherElsevier B.V.
dc.relation.ispartofseries149
dc.subjectBetween-block additivity
dc.subjectConservative variance estimator
dc.subjectMinimaxity
dc.subjectTreatment contrast
dc.subjectUnbiased estimator
dc.titleCausal inference from strip-plot designs in a potential outcomes framework
dc.typeArticle
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