Browsing by Author "Yuan, Miao"

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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.
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    Corporate Default Predictions and Methods for Uncertainty Quantifications
    Yuan, Miao (Virginia Tech, 2016-08-01)
    Regarding quantifying uncertainties in prediction, two projects with different perspectives and application backgrounds are presented in this dissertation. The goal of the first project is to predict the corporate default risks based on large-scale time-to-event and covariate data in the context of controlling credit risks. Specifically, we propose a competing risks model to incorporate exits of companies due to default and other reasons. Because of the stochastic and dynamic nature of the corporate risks, we incorporate both company-level and market-level covariate processes into the event intensities. We propose a parsimonious Markovian time series model and a dynamic factor model (DFM) to efficiently capture the mean and correlation structure of the high-dimensional covariate dynamics. For estimating parameters in the DFM, we derive an expectation maximization (EM) algorithm in explicit forms under necessary constraints. For multi-period default risks, we consider both the corporate-level and the market-level predictions. We also develop prediction interval (PI) procedures that synthetically take uncertainties in the future observation, parameter estimation, and the future covariate processes into account. In the second project, to quantify the uncertainties in the maximum likelihood (ML) estimators and compute the exact tolerance interval (TI) factors regarding the nominal confidence level, we propose algorithms for two-sided control-the-center and control-both-tails TI for complete or Type II censored data following the (log)-location-scale family of distributions. Our approaches are based on pivotal properties of ML estimators of parameters for the (log)-location-scale family and utilize the Monte-Carlo simulations. While for Type I censored data, only approximate pivotal quantities exist. An adjusted procedure is developed to compute the approximate factors. The observed CP is shown to be asymptotically accurate by our simulation study. Our proposed methods are illustrated using real-data examples.