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Full-Text Articles in Physical Sciences and Mathematics
Tolerance Intervals In Random-Effects Models, Kakotan Sanogo
Tolerance Intervals In Random-Effects Models, Kakotan Sanogo
Theses and Dissertations
In the pharmaceutical setting, it is often necessary to establish the shelf life of a drug product and sometimes suitable to assess the risk of product failure at the desired expiry period. The current statistical methodology use confidence intervals for the predicted mean to establish the expiry period and prediction intervals for a predicted new assay value or a tolerance interval for a proportion of the population for use in a risk assessment. A major concern is that most methodology treat a homogeneous subpopulation, say batch, either as a fixed effect and therefore uses a fixed-effects regression model (Graybill, 1976) …
Applications Of The Bivariate Gamma Distribution In Nutritional Epidemiology And Medical Physics, Jolene Barker
Applications Of The Bivariate Gamma Distribution In Nutritional Epidemiology And Medical Physics, Jolene Barker
Theses and Dissertations
In this thesis the utility of a bivariate gamma distribution is explored. In the field of nutritional epidemiology a nutrition density transformation is used to reduce collinearity. This phenomenon will be shown to result due to the independent variables following a bivariate gamma model. In the field of radiation oncology paired comparison of variances is often performed. The bivariate gamma model is also appropriate for fitting correlated variances. A method for simulating bivariate gamma random variables is presented. This method is used to generate data from several bivariate gamma models and the asymptotic properties of a test statistic, suggested for …
Variable Selection In Competing Risks Using The L1-Penalized Cox Model, Xiangrong Kong
Variable Selection In Competing Risks Using The L1-Penalized Cox Model, Xiangrong Kong
Theses and Dissertations
One situation in survival analysis is that the failure of an individual can happen because of one of multiple distinct causes. Survival data generated in this scenario are commonly referred to as competing risks data. One of the major tasks, when examining survival data, is to assess the dependence of survival time on explanatory variables. In competing risks, as with ordinary univariate survival data, there may be explanatory variables associated with the risks raised from the different causes being studied. The same variable might have different degrees of influence on the risks due to different causes. Given a set of …
Probe Level Analysis Of Affymetrix Microarray Data, Richard Ellis Kennedy
Probe Level Analysis Of Affymetrix Microarray Data, Richard Ellis Kennedy
Theses and Dissertations
The analysis of Affymetrix GeneChip® data is a complex, multistep process. Most often, methodscondense the multiple probe level intensities into single probeset level measures (such as RobustMulti-chip Average (RMA), dChip and Microarray Suite version 5.0 (MAS5)), which are thenfollowed by application of statistical tests to determine which genes are differentially expressed. An alternative approach is a probe-level analysis, which tests for differential expression directly using the probe-level data. Probe-level models offer the potential advantage of more accurately capturing sources of variation in microarray experiments. However, this has not been thoroughly investigated, since current research efforts have largely focused on the …