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A Generalization of The Partition Pr...

Zhou, Jie.

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A Generalization of The Partition Problem in Statistics.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A Generalization of The Partition Problem in Statistics./
作者:	Zhou, Jie.
面頁冊數:	86 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
Contained By:	Dissertation Abstracts International75-07B(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3586679
ISBN:	9781303824128

A Generalization of The Partition Problem in Statistics.
Zhou, Jie.

A Generalization of The Partition Problem in Statistics. - 86 p.

Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.

Thesis (Ph.D.)--University of New Orleans, 2013.

In this dissertation, the problem of partitioning a set of treatments with respect to a control treatment is considered. Since early 1950's a number of researchers have worked on this problem and have proposed competing alternative solutions to this statistical problem. In Tong (1979), the author proposed a formulation to solve this problem and since then hundreds of researchers and practitioners have used that formulation for the partition problem. However, Tong's formulation is somewhat rigid and unpractical for the practitioners if the distance between the "good" and the "bad" treatments is large. Under such a scenario, the indifference zone gets quite large and the undesirable feature of the Tong's formulation to partition the populations in the indifference zone, without any penalty, can potentially lead Tong's formulation to produce misleading or unpractical partitions. In this dissertation, a generalization of the Tong's formulation is proposed, under which, the treatments in the indifference zone are not partitioned as "good" or "bad", but are partitioned as a identifiable set. For this generalized partition, a fully sequential and a two-stage procedure is proposed and its theoretical properties are derived. The proposed procedures are also studied via Monte Carlo Simulation studies. The thesis concludes with some non-parametric partition procedures and the study of robustness of the various available procedures in the statistical literature.

ISBN: 9781303824128Subjects--Topical Terms:

517247
Statistics.

A Generalization of The Partition Problem in Statistics.
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500 $a Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
500 $a Adviser: Tumulesh Solanky.
502 $a Thesis (Ph.D.)--University of New Orleans, 2013.
520 $a In this dissertation, the problem of partitioning a set of treatments with respect to a control treatment is considered. Since early 1950's a number of researchers have worked on this problem and have proposed competing alternative solutions to this statistical problem. In Tong (1979), the author proposed a formulation to solve this problem and since then hundreds of researchers and practitioners have used that formulation for the partition problem. However, Tong's formulation is somewhat rigid and unpractical for the practitioners if the distance between the "good" and the "bad" treatments is large. Under such a scenario, the indifference zone gets quite large and the undesirable feature of the Tong's formulation to partition the populations in the indifference zone, without any penalty, can potentially lead Tong's formulation to produce misleading or unpractical partitions. In this dissertation, a generalization of the Tong's formulation is proposed, under which, the treatments in the indifference zone are not partitioned as "good" or "bad", but are partitioned as a identifiable set. For this generalized partition, a fully sequential and a two-stage procedure is proposed and its theoretical properties are derived. The proposed procedures are also studied via Monte Carlo Simulation studies. The thesis concludes with some non-parametric partition procedures and the study of robustness of the various available procedures in the statistical literature.
520 $a Keywords: Control population, Correct partition, Nonparametric procedure, Probability of correct decision, Sequential Procedure, Two-stage procedure, Monte Carlo simulations.
590 $a School code: 0108.
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