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Maximin efficiency robust tests for ...

Bonangelino, Pablo Esteban.

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Maximin efficiency robust tests for the focused clustering of disease.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Maximin efficiency robust tests for the focused clustering of disease./
作者:	Bonangelino, Pablo Esteban.
面頁冊數:	171 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3350.
Contained By:	Dissertation Abstracts International64-07B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3099589

Maximin efficiency robust tests for the focused clustering of disease.
Bonangelino, Pablo Esteban.

Maximin efficiency robust tests for the focused clustering of disease. - 171 p.

Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3350.

Thesis (Ph.D.)--The George Washington University, 2003.

Score tests are among the most powerful of tests for focused clustering. However, their dependency on a chosen stochastic model and specific parameterization of the exposure indicates that they will be inefficient if these assumptions are far from the truth. To address the parameterization of the exposure, we develop maximin efficiency robust tests (MERTs) for candidate sets of possible exposures. The power of several MERTs and single tests are compared through simulation. We then examine the power of our MERTs and single tests for a departure from the usual Poisson stochastic model, namely, the zero-inflated or Poisson with added zeros model. Variations of the tests, adequate for this model, are proposed. Finally, we present an application of the methods to leukemia incidence data from upstate New York.Subjects--Topical Terms:

517247
Statistics.

Maximin efficiency robust tests for the focused clustering of disease.
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500 $a Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3350.
500 $a Directors: Thomas Louis; John M. Lachin.
502 $a Thesis (Ph.D.)--The George Washington University, 2003.
520 $a Score tests are among the most powerful of tests for focused clustering. However, their dependency on a chosen stochastic model and specific parameterization of the exposure indicates that they will be inefficient if these assumptions are far from the truth. To address the parameterization of the exposure, we develop maximin efficiency robust tests (MERTs) for candidate sets of possible exposures. The power of several MERTs and single tests are compared through simulation. We then examine the power of our MERTs and single tests for a departure from the usual Poisson stochastic model, namely, the zero-inflated or Poisson with added zeros model. Variations of the tests, adequate for this model, are proposed. Finally, we present an application of the methods to leukemia incidence data from upstate New York.
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650 4 $a Biology, Biostatistics. $3 1018416
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773 0 $t Dissertation Abstracts International $g 64-07B.
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790 1 0 $a Lachin, John M., $e advisor
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