Browsing by Author "Xie, Yimeng"

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    Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection
    Xie, Yimeng (Virginia Tech, 2016-06-30)
    This dissertation focuses on three research projects: 1) construction of simultaneous prediction intervals/bounds for at least k out of m future observations; 2) semi-parametric degradation model for accelerated destructive degradation test (ADDT) data; and 3) spatial variable selection and application to Lyme disease data in Virginia. Followed by the general introduction in Chapter 1, the rest of the dissertation consists of three main chapters. Chapter 2 presents the construction of two-sided simultaneous prediction intervals (SPIs) or one-sided simultaneous prediction bounds (SPBs) to contain at least k out of m future observations, based on complete or right censored data from (log)-location-scale family of distributions. SPI/SPB calculated by the proposed procedure has exact coverage probability for complete and Type II censored data. In Type I censoring case, it has asymptotically correct coverage probability and reasonably good results for small samples. The proposed procedures can be extended to multiply-censored data or randomly censored data. Chapter 3 focuses on the analysis of ADDT data. We use a general degradation path model with correlated covariance structure to describe ADDT data. Monotone B-splines are used to modeling the underlying degradation process. A likelihood based iterative procedure for parameter estimation is developed. The confidence intervals of parameters are calculated using the nonparametric bootstrap procedure. Both simulated data and real datasets are used to compare the semi-parametric model with the existing parametric models. Chapter 4 studies the Lyme disease emergence in Virginia. The objective is to find important environmental and demographical covariates that are associated with Lyme disease emergence. To address the high-dimentional integral problem in the loglikelihood function, we consider the penalized quasi loglikelihood and the approximated loglikelihood based on Laplace approximation. We impose the adaptive elastic net penalty to obtain sparse estimation of parameters and thus to achieve variable selection of important variables. The proposed methods are investigated in simulation studies. We also apply the proposed methods to Lyme disease data in Virginia. Finally, Chapter 5 contains general conclusions and discussions for future work.
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    Change in Reports of Unmet Need For Help with ADL or Mobility Disabilities
    Sands, Laura P.; Yuan, Miao; Xie, Yimeng; Hong, Yili (Virginia Tech, 2015)
    Self-care (SC) and Mobility (MO) disabled older adults require the help of others to successfully complete daily tasks. Thirty percent of respondents to the 2011 NHATS survey reported unmet need for one or more SC or MO disabilities. Reports of unmet need for disabilities is associated with: Future hospitalization¹ Readmission² Emergency Department use³ Mortality⁴ Little is known about patterns of unmet need over time, especially the degree to which unmet need resolves, varies, or begins. Determination of predictors of change in unmet need status would inform the development of interventions to reduce unmet need.