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Now showing items 29-48 of 108

    • Estimation and Inference for Network Data 

      Lubold, Shane
      Networks play a key role in many scientific domains. In this thesis, we analyze several important questions in network analysis. The first question we analyze concerns how to understand latent structure in networks. ...
    • Estimation and Inference in Changepoint Models 

      Jewell, Sean William
      This thesis is motivated by statistical challenges that arise in the analysis of calcium imaging data, a new technology in neuroscience that makes it possible to record from huge numbers of neurons at single-neuron resolution. ...
    • Estimation and Testing Following Model Selection 

      Meir, Amit Nathan
      The field of post-selection inference focuses on developing solutions for problems in which a researcher uses a single dataset to both identify a promising set of hypotheses and conduct statistical inference. One promising ...
    • Estimation and testing under shape constraints 

      Laha, Nilanjana
      This thesis consists of three projects, the common thread to all of which is using shape-restricted densities in inference problems. In the first project, we revisit the problem of estimating the center of symmetry of ...
    • Exponential Family Models for Rich Preference Ranking Data 

      Wagner, Annelise
      Preferences can be found in a wide array of contexts, from recommender systems, to opinion polls, consumer habits, and elections. The specific method of data collection, and the types of data collected can greatly vary the ...
    • Finite Population Inference for Causal Parameters 

      Loh, Wen Wei
      Randomized experiments are often employed to determine whether a treatment X has a causal effect on an outcome Y. Under the Neyman-Rubin causal model with binary X and Y, each patient is characterized by two binary potential ...
    • Finite Sampling Exponential Bounds 

      Greene, Evan
      This dissertation develops new exponential bounds for the tail of the hypergeometric distribution. It is organized as follows. In Chapter 1, it reviews existing exponential bounds used to control the hypergeometric tail. ...
    • Fitting Stochastic Epidemic Models to Multiple Data Types 

      Tang, Mingwei
      Traditional infectious disease epidemiology focuses on fitting deterministic and stochastic epidemics models to surveillance case count data. Recently, researchers began to make use of infectious disease agent genetic data ...
    • Functional Quantitative Genetics and the Missing Heritability Problem 

      Sverdlov, Serge
      In classical quantitative genetics, the correlation between the phenotypes of individuals with unknown genotypes and a known pedigree relationship is expressed in terms of probabilities of IBD states. In existing models ...
    • Generalization of boosting algorithms and applications of Bayesian inference for massive datasets 

      Ridgeway, Gregory Kirk, 1973- (1999)
      In recent years statisticians, computational learning theorists, and engineers have developed more advance techniques to learn complex non-linear relationships from datasets. However, not only have models increased in ...
    • Generalized linear mixed models: development and comparison of different estimation methods 

      Nelson, Kerrie P (2002)
      The use of generalized linear mixed models is growing in popularity in the modelling of correlated data. To date, methods available are either computationally intensive or asymptotically biased. The following work examines ...
    • Genetic restoration on complex pedigrees 

      Sheehan, Nuala A. (Nuala Ann), 1959- (1990)
      Analyses of genetic data observed on groups of related individuals frequently require the computation of probabilities on pedigrees. Existing methods are computationally intensive and can be infeasible on large and complex ...
    • Geometric algorithms for interpretable manifold learning 

      Koelle, Samson Jonathan
      This thesis proposes several algorithms in the area of interpretable unsupervised learning.Chapters 3 and 4 introduce a sparse convex regression approach for identifying local diffeomor- phisms from a dictionary of ...
    • Gravimetric Anomaly Detection using Compressed Sensing 

      Kappedal, Ryan D.
      We address the problem of identifying underground anomalies (e.g. holes) based on gravity measurements. This is a theoretically well-studied yet difficult problem. In all except a few special cases, the inverse problem has ...
    • Improving Uncertainty Quantification and Visualization for Spatiotemporal Earthquake Rate Models for the Pacific Northwest 

      Schneider, Max
      The Pacific Northwest (PNW) has substantial earthquake risk, both due to the offshore Cascadia megathrust fault but also other fault systems that produce earthquakes under the region's population centers. Forecasts of ...
    • Inference for High-Dimensional Instrumental Variables Regression 

      Gold, David Ariel
      This thesis concerns statistical inference for the components of a high-dimensional regression parameter despite possible endogeneity of each regressor. Given a first-stage linear model for the endogenous regressors and a ...
    • Inferring Network Structure From Partially Observed Graphs 

      Pan, Mengjie
      Collecting social network data is notoriously difficult, meaning that indirectly observed or missing observations are very common. In this dissertation, We address two of such scenarios: inference on network measures without ...
    • Interpretation and Validation for Unsupervised Learning 

      zhang, hanyu
      This thesis studies two major problems in unsupervised learning: manifold learning and clustering. The motivation of this research is to establish mathematically rigorous methods that enable practitioners to have better ...
    • Large-Scale B Cell Receptor Sequence Analysis Using Phylogenetics and Machine Learning 

      Dhar, Amrit
      The adaptive immune system synthesizes antibodies, the soluble form of B cell receptors (BCRs), to bind to and neutralize pathogens that enter our body. B cells are able to generate a diverse set of high affinity antibodies ...
    • Latent models for cross-covariance 

      Wegelin, Jacob A (2001)
      Cross-covariance problems arise in the analysis of multivariate data that can be divided naturally into two blocks of variables, X and Y, observed on the same units. In a cross-covariance problem we are interested, not in ...