Now showing items 37-50 of 50

    • R-squared inference under non-normal error 

      Xu, Lei
      Assessment of the relationship between diet and health status, especially association between diet and chronic disease risk, has attracted lot of research interest in statistical and epidemiologic studies. However, due to ...
    • R-squared inference under non-normal error 

      Xu, Lei
      Assessment of the relationship between diet and health status, especially association between diet and chronic disease risk, has attracted lot of research interest in statistical and epidemiologic studies. However, due to ...
    • A resampling approach to clustering with confidence 

      Chiam, Yuan (2012-09-13)
      We propose a method for estimating the number of groups in a data set. Our method is an extension of Generalized Single Linkage clustering (GSL) (Stuetzle and Nugent 2010), a nonparametric clustering method based on the ...
    • Robust estimation of factor models in finance 

      Bailer, Heiko Manfred (2005)
      Standard asset-pricing models entail expressions for expected returns in terms of coefficients relative to risk factors. Methods to estimate premiums of risk factors have, at its core, a single or multiple linear regression ...
    • Scalable Manifold Learning and Related Topics 

      McQueen, James
      The subject of manifold learning is vast and still largely unexplored. As a subset of unsupervised learning it has a fundamental challenge in adequately defining the problem but whose solution is to an increasingly important ...
    • Scalable Methods for the Inference of Identity by Descent 

      Grimson, Fiona
      Identity by descent (IBD) describes the shared inheritance of DNA and underlies genetic similarity between individuals. Estimated IBD graphs describing the IBD relationships among individuals have many uses in statistical ...
    • Shape-Constrained Inference for Concave-Transformed Densities and their Modes 

      Doss, Charles R. (2013-11-14)
      We consider inference about functions estimated via shape constraints based on concavity. We consider log-concave densities and other “concave-transformed” densities on the real line, where a concave-transformed class is ...
    • Space-Time Smoothing Models for Surveillance and Complex Survey Data 

      Mercer, Laina D.
      Area and time-specific estimates of disease rates, cause-specific mortality rates and other key health indicators are of great interest for health care and policy purposes. Such estimates provide the information needed to ...
    • Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling 

      McDavid, Andrew
      This dissertation describes a set of statistical methods developed for analysis of single cell gene expression. A characteristic of single cell expression is bimodal expression, in which two clusters of expression are ...
    • Statistical inference using Kronecker structured covariance 

      Volfovsky, Alexander (2013-11-14)
      We present results for testing and estimation in the context of separable covariance models. We concentrate on two types of data: relational data and cross-classified data. Relational data is frequently represented by a ...
    • Testing Independence in High Dimensions & Identifiability of Graphical Models 

      Leung, Dennis
      In this thesis two problems in multivariate statistics will be studied. In the first chaper, we treat the problem of testing independence between m continuous observations when m can be larger than the available sample ...
    • Tests for Differences between Least Squares and Robust Regression Parameter Estimates and Related Topics 

      Maravina, Tatiana A. (2013-04-17)
      At the present time there is no well accepted test for comparing least squares and robust linear regression coefficient estimates. To fill this gap we propose and demonstrate the efficacy of two Wald-like statistical tests ...
    • Theory and Methods for Tensor Data 

      Gerard, David C.
      We present novel methods and new theory in the statistical analysis of tensor-valued data. A tensor is a multidimensional array. When data come in the form of a tensor, special methods and models are required to capture ...
    • The weighted likelihood bootstrap and an algorithm for prepivoting 

      Newton, Michael A. (Michael Abbott), 1964- (1991)
      The method of bootstrapping, which has transformed the theory and practice of frequentist statistical inference, is applicable within the Bayesian paradigm. Rather than simulating data that might have been observed, this ...