Browse Dissertations and Theses - Statistics by Title

  • Song, Yanglei (2019-04-05)
    Sequential analysis refers to the statistical theory and methods that can be applied to situations where the sample size is not fixed in advance. Instead, the data are collected sequentially over time, and the sampling is ...

    application/pdf

    application/pdfPDF (1MB)
  • Kundu, Subrata (1994)
    Let $X\sb1, X\sb2, \... X\sb{n}$ be i.i.d random variables with common unknown density function f. Here we are interested in estimating the unknown density f with bounded Mean Integrated Absolute Error (MIAE). Devroye and ...

    application/pdf

    application/pdfPDF (2MB)Restricted to U of Illinois
  • Shen, Liji (1994)
    Local kernel estimates and B-spline estimates are considered in the nonparametric regression and the generalized linear models. The consistency and asymptotic normality of kernel estimates are proved. Simulations on B-spline ...

    application/pdf

    application/pdfPDF (2MB)Restricted to U of Illinois
  • Huang, Weihong (2017-06-27)
    Computational statistics, including methods such as Markov chain Monte Carlo (MCMC), bootstrap, approximate Bayesian computation, is an important part in modern statistics and has been widely used in many areas, such as ...

    application/pdf

    application/pdfPDF (4MB)Restricted Access
  • Sengupta, Srijan (2016-07-08)
    This dissertation is divided into two parts, concerning two areas of statistical methodology. The first part of this dissertation concerns statistical analysis of networks with community structure. The second part of this ...

    application/pdf

    application/pdfPDF (1MB)
  • Junker, Brian William (1988)
    Current achievement and aptitude test modeling--item response theory--is based on the overly-optimistic assumption of local independence: that examinee's responses to different test questions will be independent conditional ...

    application/pdf

    application/pdfPDF (5MB)Restricted to U of Illinois
  • Yang, Fan (2018-04-17)
    This dissertation is devoted to statistical inference based on characteristic functions. For some popular stochastic processes (e.g., Lévy processes, Lévy driven Ornstein-Uhlenbeck processes), the transition density may ...

    application/pdf

    application/pdfPDF (2MB)Restricted Access
  • Zhang, Xianyang (2013-08-22)
    Functional data Analysis has emerged as an important area of statistics which provides convenient and informative tool for the analysis of data objects of high dimension/high resolution. In the literature, it seems that ...

    application/pdf

    application/pdfPDF (1MB)
  • Lee, Chung Eun (2017-06-30)
    In this thesis, we focus on inference problems for time series and functional data and develop new methodologies by using new dependence metrics which can be viewed as an extension of Martingale Difference Divergence (MDD) ...

    application/pdf

    application/pdfPDF (2MB)Restricted to U of Illinois
  • Zhang, Jingfei (2014-05-30)
    Networks arise from modeling complex systems in various fields, such as computer science, social science, biology, psychology and finance. Understanding and analyzing networks help us better understand these complex systems ...

    application/pdf

    application/pdfPDF (6MB)
  • Fan, Zhewen (2011-01-14)
    Chapter 1 is concerned with confidence interval construction for the mean of a long-range dependent time series. It is well known that the moving block bootstrap method produces an inconsistent estimator of the distribution ...

    application/pdf

    application/pdfPDF (2MB)
  • Xia, Jing (2011-05-25)
    Since the early 1990s, functional magnetic resonance imaging (fMRI) has dominated the brain mapping field and it has been proved a powerful tool for mapping human brain functions. The fMRI is a high spatial-temporal ...

    application/pdf

    application/pdfPDF (3MB)
  • Hu, Jianjun (2017-04-20)
    With the fast development of networking, data storage, and the data collection capacity, big data are now rapidly expanding in all science and engineering domains. When dealing with such data, it is appealing if we can ...

    application/pdf

    application/pdfPDF (1MB)
  • Dalpiaz, David (2014-09-16)
    This thesis explores various methods for analyzing data generated using the next-generation sequencing technology, RNA-Seq. Two methods are developed which attempt to accurately calculate RNA expression, the first using a ...

    application/pdf

    application/pdfPDF (4MB)
  • Liu, Yufei (2013-08-22)
    This dissertation is centered on the modeling of heterogeneous data which is ubiquitous in this digital information age. From the statistical point of view heterogeneous data is composed of dissimilar components, where ...

    application/pdf

    application/pdfPDF (2MB)
  • Yang, Ji Yeon (2010-08-31)
    The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational ...

    application/pdf

    application/pdfPDF (1MB)
  • Sewell, Daniel K (2015-04-21)
    Dyadic data are ubiquitous and arise in the fields of biology, epidemiology, sociology, and many more. Such dyadic data are often best understood within the framework of networks. Network data can vary in many ways. For ...

    application/pdf

    application/pdfPDF (9MB)
  • Li, Bin (2013-08-22)
    Nowadays in many statistical applications, we face models whose complexity increases with the sample size. Such models pose a challenge to the traditional statistical analysis, and call for new methodologies and new ...

    application/pdf

    application/pdfPDF (3MB)
  • application/pdf

    application/pdfPDF (2MB)Restricted to U of Illinois
  • Douglas, Jeffrey A. (1995)
    The simultaneous and nonparametric estimation of latent abilities and item characteristic curves is considered. In particular, the joint asymptotic properties of ordinal ability estimation and kernel smoothed nonparametric ...

    application/pdf

    application/pdfPDF (5MB)Restricted to U of Illinois