Kernel-Based Interval Estimates of Inverse Dose Response With Applications to Genetic Clines

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Kernel-Based Interval Estimates of Inverse Dose Response With Applications to Genetic Clines

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dc.contributor.advisor Jernigan, Robert W en_US
dc.contributor.author Lo, Annie en_US
dc.contributor.other Nolan, John P en_US
dc.contributor.other Jackson, Monica C en_US
dc.date.accessioned 2012-08-22T15:01:16Z
dc.date.available 2012-08-22T15:01:16Z
dc.date.created 2011 en_US
dc.date.issued 2012-08-22
dc.identifier.other Lo_american_0008D_10040 en_US
dc.identifier.uri http://hdl.handle.net/1961/11071
dc.description Degree awarded: Ph.D. Mathematics and Statistics. American University en_US
dc.description.abstract We investigate nonparametric regression techniques to estimate the distribution of the LD<sub>100&alpha;</sub>, 0 <&alpha; < 1, the lethal dose where 100&alpha;% of subjects show a response. Kernel methods are used to estimate the resulting response probability curve using real and simulated data. We apply and extend these kernel-based estimation procedures to a problem in evolutionary genetics where the prevalence of a genetic trait is mapped. In this setting, distance serves as a dose and the response probability curve is called a cline. We investigate the distributional properties of kernel estimates of LD<sub>100&alpha;</sub> with special attention to the LD<sub>20</sub>, LD<sub>80</sub>, and the distance between them, called the cline width. Confidence intervals are constructed for LD<sub>20</sub>, LD<sub>80</sub>, and the cline width and small sample properties are investigated through series expansion and simulation. en_US
dc.format.extent 116 p. en_US
dc.format.mimetype application/pdf en_US
dc.publisher American University en_US
dc.subject Statistics en_US
dc.subject.other cline width en_US
dc.subject.other Genetic clines en_US
dc.subject.other Kernel estimation en_US
dc.title Kernel-Based Interval Estimates of Inverse Dose Response With Applications to Genetic Clines en_US
dc.type Text en_US
dc.type Dissertation en_US


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