These digital collections include theses, dissertations, and datasets from the Department of Statistics.

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  • Some topics in high-dimensional robust inference and graphical modeling 

    Author(s):Song, Youngseok
    Date Issued:2021
    Format:born digital; doctoral dissertations
    In this dissertation, we focus on large-scale robust inference and high-dimensional graphical modeling. Especially, we study three problems: a large-scale inference method by a tail-robust regression, model specification ...
  • Bayesian methods for spatio-temporal ecological processes using imagery data 

    Author(s):Lu, Xinyi
    Date Issued:2021
    Format:born digital; doctoral dissertations
    In this dissertation, I present novel Bayesian hierarchical models to statistically characterize spatio-temporal ecological processes. I am motivated by the volatility of Alaskan ecosystems in the face of global climate ...
  • Some topics on survey estimators under shape constraints 

    Author(s):Xu, Xiaoming
    Date Issued:2021
    Format:born digital; doctoral dissertations
    We consider three topics in this dissertation: 1) Nonresponse weighting adjustment using estimated response probability; 2) Improved variance estimation for inequality constrained domain mean estimators in surveys; and 3) ...
  • Heavy tail analysis for functional and internet anomaly data 

    Author(s):Kim, Mihyun
    Date Issued:2021
    Format:born digital; doctoral dissertations
    This dissertation is concerned with the asymptotic theory of statistical tools used in extreme value analysis of functional data and internet anomaly data. More specifically, we study four problems associated with analyzing ...
  • Bayesian treed distributed lag models 

    Author(s):Mork, Daniel S.
    Date Issued:2021
    Format:born digital; doctoral dissertations
    In many applications there is interest in regressing an outcome on exposures observed over a previous time window. This frequently arises in environmental epidemiology where either a health outcome on one day is regressed ...
  • Penalized unimodal spline density estimate with application to M-estimation 

    Author(s):Chen, Xin
    Date Issued:2020
    Format:born digital; doctoral dissertations
    This dissertation establishes a novel type of robust estimation, Auto-Adaptive M-estimation (AAME), based on a new density estimation. The new robust estimation, AAME, is highly data-driven, without the need of priori of ...
  • Non-asymptotic properties of spectral decomposition of large gram-type matrices with applications to high-dimensional inference 

    Author(s):Zhang, Lyuou
    Date Issued:2020
    Format:born digital; doctoral dissertations
    Jointly modeling a large and possibly divergent number of temporally evolving subjects arises ubiquitously in statistics, econometrics, finance, biology, and environmental sciences. To circumvent the challenges due to the ...
  • Data associated with "Interpersonal relationships drive successful team science: an exemplary case-based study" 

    Author(s):Love, Hannah; Cross, Jennifer; Fosdick, Bailey; Crooks, Kevin; VandeWoude, Susan; Fisher, Ellen
    Date:2020
    Format:ZIP; PDF; CSV
    Team science, or collaborations between groups of scientists with varying expertise, is required for researching solutions to complex problems of the 21st century. Despite the essential need for such transdisciplinary ...
  • Nonparametric tests for informative selection and small area estimation for reconciling survey estimates 

    Author(s):Liu, Teng
    Date Issued:2020
    Format:born digital; doctoral dissertations
    Two topics in the analysis of complex survey data are addressed: testing for informative selection and addressing temporal discontinuities due to survey redesign. Informative selection, in which the distribution of response ...
  • Statistical modeling and inference for complex-structured count data with applications in genomics and social science 

    Author(s):Cao, Meng
    Date Issued:2020
    Format:born digital; doctoral dissertations
    This dissertation describes models, estimation methods, and testing procedures for count data that build upon classic generalized linear models, including Gaussian, Poisson, and negative binomial regression. The methodological ...
  • Statistical modeling and inference for spatial and spatio-temporal data 

    Author(s):Liu, Jialuo
    Date Issued:2019
    Format:born digital; doctoral dissertations
    Spatio-temporal processes with a continuous index in space and time are encountered in many scientific disciplines such as climatology, environmental sciences, and public health. A fundamental component for modeling such ...
  • Regression of network data: dealing with dependence 

    Author(s):Marrs, Frank W.
    Date Issued:2019
    Format:born digital; doctoral dissertations
    Network data, which consist of measured relations between pairs of actors, characterize some of the most pressing problems of our time, from environmental treaty legislation to human migration flows. A canonical problem ...
  • Outlier discordancy tests based on saddlepoint approximations 

    Author(s):Sleeper, Andrew D.
    Date Issued:2019
    Format:born digital; doctoral dissertations
    When testing for the discordancy of a single observed value, a test based on large values of the maximum absolute studentized residual (MASR) or maximum squared studentized residual (MSSR) is known to be optimal, by ...
  • Dataset associated with "Laboratory evaluation of low-cost PurpleAir PM monitors and in-field correction using co-located portable filter samplers" 

    Author(s):Tryner, Jessica; L'Orange, Christian; Mehaffy, John; Miller-Lionberg, Daniel; Hofstetter, Josephine C.; Wilson, Ander; Volckens, John
    Date:2019
    Format:ZIP; TXT; PDF; CSV
    Low-cost aerosol monitors can provide more spatially- and temporally-resolved data on ambient fine particulate matter (PM2.5) concentrations than are typically available from regulatory monitoring networks; however, low-cost ...
  • Statistical modeling and computing for climate data 

    Author(s):Hewitt, Joshua
    Date Issued:2019
    Format:born digital; doctoral dissertations
    The motivation for this thesis is to provide improved statistical models and approaches to statistical computing for analyzing climate patterns over short and long distances. In particular, information needs for water ...
  • Statistical upscaling of stochastic forcing in multiscale, multiphysics modeling 

    Author(s):Vollmer, Charles T.
    Date Issued:2019
    Format:born digital; doctoral dissertations
    Modeling nuclear radiation damage necessarily involves multiple scales in both time and space, where molecular-level models have drastically different assumptions and phenomena than continuumlevel models. In this thesis, ...
  • Advances in statistical analysis and modeling of extreme values motivated by atmospheric models and data products 

    Author(s):Fix, Miranda J.
    Date Issued:2018
    Format:born digital; doctoral dissertations
    This dissertation presents applied and methodological advances in the statistical analysis and modeling of extreme values. We detail three studies motivated by the types of data found in the atmospheric sciences, such as ...
  • Methods for extremes of functional data 

    Author(s):Xiong, Qian
    Date Issued:2018
    Format:born digital; doctoral dissertations
    Motivated by the problem of extreme behavior of functional data, we develop statistical theory at the nexus of functional data analysis (FDA) and extreme value theory (EVT). A fundamental technique of functional data ...
  • Inference for cumulative intraday return curves 

    Author(s):Zheng, Ben
    Date Issued:2018
    Format:born digital; doctoral dissertations
    The central theme of this dissertation is inference for cumulative intraday return (CIDR) curves computed from high frequency data. Such curves describe how the return on an investment evolves with time over a relatively ...
  • Improved inference in heteroskedastic regression models with monotone variance function estimation 

    Author(s):Kim, Soo Young
    Date Issued:2018
    Format:born digital; doctoral dissertations
    The problems associated with heteroskedasticity often lead to incorrect inferences in a regression model, especially when the form of the heteroskedasticity is obscure. In this dissertation, I present methods to estimate ...

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