Browsing by Title

Sort by: Order: Results:

  • Oprea, AD; Lombard, FW; Liu, W-W; White, WD; Karhausen, JA; Li, Y-J; Miller, TE; Aronson, S; Gan, TJ; Fontes, ML; Kertai, MD (2016-12)
    BACKGROUND: Increased pulse pressure (PP) is an important independent predictor of cardiovascular outcome and acute kidney injury (AKI) after cardiac surgery. The objective of this study was to determine whether elevated ...
  • Morrissey, Meghan Ann (2015)
    <p>Basement membranes are a dense, sheet-like form of extracellular matrix that underlie epithelia and endothelia, and surround muscle, fat and Schwann cells. Basement membranes separate tissues and protect them from ...
  • Shakimov, Amre (2012)
    <p>Online Social Network (OSN) services such as Facebook and Google+ are fun and useful. Hundreds of millions of users rely on these services and third-party applications to process and share personal data such as friends ...
  • Zhu, Wenjia; Ye, Wenting (2015-04-21)
    The BASF Agricultural Solutions Headquarters occupies, the client of this project, has requested our team to examine the biological value, environmental value and economic value of its 100 acres corporate-owned forest which ...
  • Wu, Joyce (2012)
    <p>Although Charles Baudelaire's poetry was censored in part for his graphic representations of death, for Baudelaire himself, death was the ultimate censorship. He grappled with its limitations of the possibility of ...
  • Scott, JG; Berger, JO (2010-10-01)
    This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens automatically in Bayesian ...
  • Wang, Ye (2014)
    <p>Although Bayesian density estimation using discrete mixtures has good performance in modest dimensions, there is a lack of statistical and computational scalability to high-dimensional multivariate cases. To combat the ...
  • Scott, James Gordon (2009)
    <p>This thesis is about Bayesian approaches for handling multiplicity. It considers three main kinds of multiple-testing scenarios: tests of exchangeable experimental units, tests for variable inclusion in linear regresson ...
  • Niemi, Jarad (2009)
    <p>Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally ...
  • Nakajima, J; West, M (2013-04-01)
    We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural ...
  • Leininger, Thomas Jeffrey (2014)
    <p>We explore the posterior inference available for Bayesian spatial point process models. In the literature, discussion of such models is usually focused on model fitting and rejecting complete spatial randomness, with ...
  • Chen, Minhua (2012)
    <p>The concept of sparseness is harnessed to learn a low dimensional representation of high dimensional data. This sparseness assumption is exploited in multiple ways. In the Bayesian Elastic Net, a small number of correlated ...
  • Dalzell, Nicole M. (2017)
    <p>File linking allows analysts to combine information from two or more sources of information, creating linked data bases. From linking school records to track student progress across years, to official statistics and ...
  • Shapiro, Heather (2015-05-08)
    The Billboard Hot 100 has been the main record chart for popular music in the American music industry since its first official release in 1958. Today, this rank- ing is based upon the frequency of which a song is played ...
  • Chen, Yuhan (2016)
    <p>Testing for differences within data sets is an important issue across various applications. Our work is primarily motivated by the analysis of microbiomial composition, which has been increasingly relevant and important ...
  • Chen, Xi (2017)
    <p>Streaming network data of various forms arises in many applications, raising interest in research to model and quantify the nature of stochasticity and structure in dynamics underlying such data. One example context is ...
  • Irie, Kaoru (2016)
    <p>The advances in three related areas of state-space modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key ...
  • Murray, JS; Dunson, DB; Carin, L; Lucas, JE (2013-06-01)
    Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent ...
  • Park, JH; Dunson, DB (2010-07-01)
    Starting with a carefully formulated Dirichlet process (DP) mixture model, we derive a generalized product partition model (GPPM) in which the partition process is predictor-dependent. The GPPM generalizes DP clustering ...
  • Li, Yingbo (2013)
    <p>With the development of modern data collection approaches, researchers may collect hundreds to millions of variables, yet may not need to utilize all explanatory variables available in predictive models. Hence, choosing ...