Now showing items 18-37 of 79

    • Discovering Interactions in Multivariate Time Series 

      Tank, Alex
      In large collections of multivariate time series it is of interest to determine interactions between each pair of time series. Classically, interactions between time series have been studied using linear vector autoregressive ...
    • Discrete-Time Threshold Regression for Survival Data with Time-Dependent Covariates 

      Sharkansky, Stefan
      A natural approach to survival analysis in many settings is to model the subject's ``health'' status as a latent stochastic process, where the terminal event is represented by the first time that the process crosses a ...
    • Estimating Population Size Using the Network Scale Up Method 

      Maltiel, Rachael (2013-07-25)
      We develop methods for estimating hard-to-reach populations from data collected using network-based questions on standard surveys. Such data arise by asking respondents how many people they know in a specific group (e.g. ...
    • 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 ...
    • 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 ...
    • 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 ...
    • 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 ...
    • 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 ...
    • Latent Variable Models for Indirectly or Imprecisely Measured Networks 

      Lee, Wesley
      In many scientific settings, networks are important structures used to represent the relationships between actors in a population of study. The most common methods for measuring networks are to survey study participants ...
    • Learning and Manifolds: Leveraging the Intrinsic Geometry 

      Perrault-Joncas, Dominique Chipman (2013-07-23)
      In this work, we explore and exploit the use of differential operators on manifolds - the Laplace-Beltrami operator in particular - in learning tasks. In particular, we are interested in uncovering the geometric structure ...