Now showing items 13-32 of 46

    • 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. ...
    • 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. ...
    • 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 ...
    • 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 ...
    • 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 ...
    • The Likelihood Pivot: Performing Inference with Confidence 

      Harmon, James Warren
      Maximum likelihood estimation is a popular statistical method. To account for possible model misspecification, the sandwich estimate of variance can be used to generate asymptotically correct confidence intervals. Several ...
    • Likelihood-Based Inference for Partially Observed Multi-Type Markov Branching Processes 

      Xu, Jason
      Markov branching processes are a class of continuous-time Markov chains (CTMCs) frequently used in stochastic modeling with ubiquitous applications. Bivariate or multi-type processes are necessary to model phenomena such ...
    • Lord's Paradox and Targeted Interventions: The Case of Special Education 

      Theobald, Roderick Jenkins
      Lord (1967) describes a hypothetical “paradox” in which two statisticians, analyzing the same dataset using different but defensible methods, come to very different conclusions about the effects of an intervention on student ...
    • Maximum likelihood estimation in Gaussian AMP chain graph models and Gaussian ancestral graph models 

      Drton, Mathias, 1975- (2004)
      Graphical Markov models use graphs to represent dependencies between stochastic variables. Via Markov properties, missing edges in the graph are translated into conditional independence statements, which, in conjunction ...
    • Modeling Heterogeneity within and between Matrices and Arrays 

      Fosdick, Bailey Kathryn (2013-11-14)
      Datasets in the form of matrices and arrays arise frequently in the social and biological sciences and are characterized by measurements indexed by two or more factors. In this dissertation we address two problems relating ...
    • Monte Carlo estimation of identity by descent in populations 

      Glazner, Chris
      Genetic similarity between organisms arises from segments of shared genome, which are said to be identical by descent (IBD). Modeling IBD in pedigrees forms the basis of classical linkage analysis and has been a fruitful ...
    • Monte Carlo likelihood calculation for identity by descent data 

      Browning, Sharon, 1973- (1999)
      Two individuals are identical by descent at a genetic locus if they share the same gene copy at that locus due to inheritance from a recent common ancestor. Identity by descent can be thought of as a continuous process ...
    • Phylogenetic Stochastic Mapping 

      Irvahn, Jan
      Phylogenetic stochastic mapping is a method for reconstructing the history of trait changes on a phylogenetic tree relating species/organisms carrying the trait. State-of-the-art methods assume that the trait evolves ...
    • Portfolio Optimization with Tail Risk Measures and Non-Normal Returns 

      Zhu, Minfeng (2010-08-20)
      The traditional Markowitz mean-variance portfolio optimization theory uses volatility as the sole measure of risk. However, volatility is flawed both intuitively and theoretically: being symmetric it does not differentiate ...