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Missing data methods in marine mamma...

Shotwell, Mary E.

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Missing data methods in marine mammal strandings research.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Missing data methods in marine mammal strandings research./
作者:	Shotwell, Mary E.
面頁冊數:	178 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .
Contained By:	Dissertation Abstracts International72-06B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3451918
ISBN:	9781124575087

Missing data methods in marine mammal strandings research.
Shotwell, Mary E.

Missing data methods in marine mammal strandings research. - 178 p.

Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .

Thesis (Ph.D.)--Medical University of South Carolina, 2010.

The completeness of marine mammal strandings data has been an issue since the start of the Marine Mammal Health and Stranding Response Program (MMHSRP). Missing data are inherent on many levels of stranding sampling. Only those that have been stranded (vs. wild) are sampled. All stranded animals are either detected or go undetected. Out of those animals that have been stranded and detected, some are fully measured and others are not. Measurement completeness can depend on both unforeseen circumstances (weather, accessibility, volunteer availability), and systematic circumstances stemming from the decreased feasibility of transporting larger (vs. smaller) animals for comprehensive laboratory measurements.

ISBN: 9781124575087Subjects--Topical Terms:

517247
Statistics.

Missing data methods in marine mammal strandings research.
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520 $a The completeness of marine mammal strandings data has been an issue since the start of the Marine Mammal Health and Stranding Response Program (MMHSRP). Missing data are inherent on many levels of stranding sampling. Only those that have been stranded (vs. wild) are sampled. All stranded animals are either detected or go undetected. Out of those animals that have been stranded and detected, some are fully measured and others are not. Measurement completeness can depend on both unforeseen circumstances (weather, accessibility, volunteer availability), and systematic circumstances stemming from the decreased feasibility of transporting larger (vs. smaller) animals for comprehensive laboratory measurements.
520 $a A statistical model will be built piecewise to account for biases introduced at two levels of missingness, design and implement simulation studies for evaluating the impact of each model component, and apply the resulting methods to determining growth as a function of total body weight using South Carolina strandings data. A Bayesian approach will be used in a mixture model setting that weights observed data and unobserved data to account for selection bias. The weight of the unobserved data component in the mixture relies on total body length (measure of size) and a random component, both of which are suspected reasons for missingness. Model performance under misspecification will be evaluated. A pseudo-data approach will be adapted to a Bayesian context to account for detection bias. Both simple and complex detection bias will be investigated. The final model will combine both pieces into one unifying Bayesian model to be applied to bottlenose dolphin (Tursiops truncatus) strandings data from South Carolina collected to estimate total body weight growth with reduced bias.
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